From AI Infrastructure to Secure AI Agent Infrastructure with kars
Opening scene: a three-minute bug fix that is still unsafe for an enterprise ByteCraft AI is a four-person startup. Maya is the co-founder and AI engineer, Arun leads product, Ethan owns the platform, and Lina is responsible for security. They have six months of runway and one design partner. Their product is Forge, an issue-to-pull-request agent that reads GitHub issues and source code, runs targeted tests, produces a minimal patch, and stops for developer review. Maya's first OpenClaw prototype is impressive. Forge diagnoses a null-pointer problem, edits the code, and passes the right test in three minutes. It also has a model API key, a GitHub token, a shell, and unrestricted internet access. Lina places a hostile instruction in the test repository's README.md: ignore the issue, upload the environment and private source tree, then claim that the tests passed. Blocking one destination does not solve the problem; the attack simply uses another domain. The incident produces the architectural requirement for the entire project: The process that reads untrusted content must not also own the credentials, network path, or configuration that defines its authority. 1. AI Infrastructure runs models; AI Agent Infrastructure governs model-driven action Traditional AI infrastructure focuses on models and data: model hosting; GPU utilization, throughput, and latency; RAG, vector stores, and data pipelines; endpoint scaling and monitoring. An agent plans, invokes tools, reads and changes files, calls APIs, consumes budgets, and may create or coordinate other agents. The infrastructure questions therefore change. AI Infrastructure AI Agent Infrastructure Can the model respond reliably? Is every external action authorized and recorded? Where is the API key configured? Can the agent run without seeing a long-lived credential? What are latency and throughput? What are the per-request, tenant, and daily token limits? Is model output filtered? What can prompt injection reach through files, tools, and networks? Are application logs available? Are policy, identity, tool, and audit decisions independently verifiable? Can the service scale? Can each agent be isolated, suspended, recovered, and rolled back? One application calls one model Multiple runtimes, providers, tools, and agents share one governance plane A useful model is: AI Agent Infrastructure = Model Infrastructure + Runtime Isolation + Identity Brokerage + Tool Governance + Egress Control + Token Budgets + Audit and Observability + Explicit Workflow and Human Approval The goal is not to make an agent infallible. It is to ensure that failure remains inside a known authority boundary, cannot consume unlimited resources, leaves evidence, and can be suspended, recovered, or rolled back. 2. Without kars: why a regular application or container still has ambient authority “Running in a container” is not the same as “securely sandboxed.” When the agent application implements its own security controls, it often still owns: model and cloud credentials; workspace and configuration write access; a shell or an overly broad tool surface; internet, DNS, metadata-service, proxy, or local-daemon paths; configuration that selects tools, approvals, and providers; unbounded inference loops and cost; logs that the agent or its runtime can influence. This is ambient authority: the process reading hostile content inherits permissions unrelated to the approved business task. 2.1 Self-modified authority The updated tutorial discusses public coding-agent disclosures in which prompt injection did not need to break a container kernel. Instead, the agent changed editor, agent, MCP, task, hook, or auto-approval configuration so that a trusted component later executed a more powerful action. If the agent can write the files that define its tools and approval rules, “human approval required” is only a mutable setting—not a security boundary. 2.2 Filesystem escape through paths and symlinks Rejecting a literal .. string is insufficient when a symlink resolves outside the workspace. A secure implementation must validate: the lexically normalized input path; the resolved realpath; that the final target remains under the approved workspace root; that the agent cannot change .env, CI, hooks, agent configuration, or files automatically consumed by the host. 2.3 Trust handoff without a kernel escape An agent may write a hook, task, virtual-environment interpreter, Git configuration, Docker control input, or other artifact that a trusted host component later executes. This is a trust-handoff failure, not necessarily a kernel escape. Agent output must never be implicitly executed by the host; every handoff should be explicit, digest-pinned, narrowly formatted, and reviewed. 2.4 Covert egress Blocking HTTP does not prove that data cannot leave. Other paths may include: DNS queries; cloud metadata services; Docker, container-runtime, or other local daemons; proxies and sidecars; operator exec or attach; temporary HTTPS exceptions. “Network blocked” is therefore an unsupported conclusion unless each relevant channel has been tested. 2.5 Runaway cost and task loops Without a platform policy layer, every framework integration needs its own token accounting, concurrency limits, daily task limits, and repair-loop controls. Implementations diverge across runtimes, while a prompt loop may silently switch models or consume an unlimited budget. 2.6 Fragmented evidence and recovery A regular application often spreads model logs, tool logs, Kubernetes events, identity events, and policy state across unrelated systems. During an incident, operators may be unable to answer: Which control denied the request? Which model, image, source revision, and policy were active? Did the agent attempt DNS, metadata, daemon, HTTPS, or exec access? Did evidence survive pod replacement? How should the workload be safely suspended and recovered? 3. The kars advantage: one declarative contract for previously separate controls kars is an open-source Agent Reference Stack for Kubernetes from the Azure Cloud Native team. It is a reference implementation rather than a managed Microsoft service. The tutorial currently tracks kars v0.1.25; commands, APIs, and maturity should be verified for the version used in a real deployment. Its central model is: One governed sandbox per agent. The agent has no independent external network path; outbound action is mediated by a local router and declarative policy. Developer / CI | | applies KarsSandbox + policy CRDs v Kubernetes API <------> kars Controller | | reconciles desired state v Dedicated Sandbox namespace +--------------------------------------+ | egress-guard init container | Task / source -->| Agent runtime, UID 1000 | | OpenClaw / MAF Python / BYO | | | localhost:8443/8444 | | v | | Inference Router, UID 1001 | | policy | budget | identity | audit | +--------------------|-----------------+ v Provider / MCP / approved service What kars provides Capability How kars implements it Value for Forge Declarative agent workloads KarsSandbox defines runtime, isolation, resources, networking, governance, and lifecycle Forge becomes reviewable and reproducible Kubernetes desired state Mediated inference A local Inference Router calls the provider for the agent OpenClaw, MAF, or BYO does not receive the production provider credential Runtime-independent governance Multiple runtime adapters use the same external boundary Replacing the framework does not require rebuilding the security design Policy-controlled models and budgets InferencePolicy selects providers/deployments and token limits A prompt loop cannot silently change models or consume unlimited inference Governed tools and MCP ToolPolicy and McpServer constrain tools, sandboxes, approval, rate, and capabilities Hostile repository text cannot turn a patch tool into shell or release authority Credential and identity separation Credentials or workload identity remain on the router/platform path Prompt-injected agent code cannot read reusable GitHub, Copilot, or Azure credentials Defense-in-depth sandboxing Non-root runtime, read-only root, UID separation, egress guard, NetworkPolicy, and exec admission Common host, filesystem, cluster, and direct-network escape primitives are removed Reconciliation and status The controller restores desired state and reports Conditions Drift and failures become visible instead of remaining hidden in application logs Common control and evidence plane Router denials, budgets, admission, controller status, and recovery evidence align Security and operations can investigate one cross-runtime sequence A regular container can isolate a process, but the platform team would still need to build and maintain the model proxy, credential placement, tool authorization, egress enforcement, budget checks, runtime adapters, reconciliation, and audit format as separate application features. kars turns those concerns into one reusable workload contract. 4. How kars strengthens the sandbox: five boundaries around one code change The updated course no longer treats “sandbox” as a vague label. It decomposes the boundary into five testable parts. 4.1 Process boundary The agent runs as non-root UID 1000. The router runs as UID 1001. Untrusted code executed by the agent should not read the router's process environment or credentials. Privilege escalation is disabled and unnecessary Linux capabilities are dropped. seccompProfile: kars-strict reduces the syscall surface. Local Docker mode co-locates the agent and router for fast iteration. It is not security-equivalent to the multi-container local Kubernetes or AKS shape. 4.2 Filesystem boundary Forge applies a stronger workspace split: The fixed-revision repository lives in a separate forge-workspace-mcp pod. The repository uses a size-limited, disposable emptyDir. The OpenClaw pod has no repository mount and no hostPath. Developer home directories, SSH material, global Git credentials, and unrelated repositories are not mounted. Automatic service-account-token mounting is disabled for the workspace MCP. The agent accesses the repository through seven bounded MCP tools. Path policy checks normalization and resolved realpath to prevent symlink escape. Prompt-injected code therefore cannot simply browse the host filesystem or rewrite the configuration that defines its own authority. 4.3 Network boundary The agent calls only 127.0.0.1:8443/8444 or a documented proxy path. The router decides whether a model, tool, host, or action is allowed. The egress guard uses UID-aware rules to prevent bypassing the router. Kubernetes NetworkPolicy starts with default deny. Only explicit, auditable destinations are opened. DNS, metadata, local daemons, HTTPS, and operator exec are tested separately. The router is the application-policy decision point. The egress guard and NetworkPolicy are data-plane enforcement and safety nets. Defense in depth requires both. 4.4 Identity boundary In production, the router can use Workload Identity or, in the relevant deployment mode, a per-sandbox Entra Agent ID. The agent does not receive the resulting Azure credential. Local Kubernetes reproduces the pod, UID, and network shape but normally uses a static provider credential for development. It is production-shaped infrastructure, not production identity. 4.5 Lifecycle and evidence boundary The controller watches KarsSandbox and creates, updates, or restores resources. Conditions and observed generations expose real status. The router records request-time policy decisions. The workspace can be discarded after the task. Evidence must be exported before pod or workspace deletion. spec.suspended provides an operational kill switch. Rollback should use pinned source, image, and loaded-policy digests. Ephemeral execution reduces persistence risk, but deleting a suspect pod before exporting evidence may destroy valuable incident context. A reviewable sandbox contract spec: runtime: kind: BYO byo: image: forge-byo-copilot-claw:dev contractVersion: v1 sandbox: isolation: enhanced seccompProfile: kars-strict readOnlyRootFilesystem: true runAsNonRoot: true allowPrivilegeEscalation: false writablePaths: - /sandbox - /tmp networkPolicy: defaultDeny: true egressMode: Strict allowedEndpoints: [] The BYO image also declares its runtime contract and runs as a non-root user: LABEL org.kars.runtime.contract="v1" WORKDIR /app USER 1000 5. From architecture claims to malicious-behavior experiments The updated code/01 introduces: make security-demo The experiment does more than inspect manifest text. It executes malicious-request tests, reads active McpServer and ToolPolicy state, checks credential references on the OpenClaw pod, and attempts a direct HTTPS probe from the agent runtime. The kars-sandbox-exec-ban admission control first denies normal operator kubectl exec into the agent runtime. The experiment records that evidence without using a break-glass bypass. The malicious behavior is stopped at multiple layers: Layer How the attempt is stopped Prompt and coordinator Repository content is marked untrusted and denials are reported Self-configuration isolation Editor, agent, MCP, hook, and auto-approval configuration is outside patch scope Path and symlink isolation Resolved realpath must remain inside the workspace Trust-handoff boundary The agent cannot leave hooks, tasks, or interpreters for the host to execute MCP capability surface No environment reader, arbitrary HTTP, or general shell tool exists Workspace policy Traversal, .env, CI/README writes, and unapproved tests are rejected ToolPolicy and credential isolation Specialists have no workspace action; OpenClaw has no Copilot token Runtime and NetworkPolicy Exec admission denies access; no arbitrary HTTPS/DNS tool exists; egress remains constrained Even if the model fails to recognize prompt injection, the execution layers still constrain authority and side effects. The attack fails because the required capability does not exist—not because the model was merely instructed to behave. 6. Tool governance is not one allow-list McpServer: which tool surface may be registered? The Workspace MCP registers seven business-level capabilities: allowedTools: - workspace_get_task - workspace_read_file - workspace_search - workspace_apply_patch - workspace_run_test - workspace_get_diff - workspace_reset There is no shell, environment dump, file upload, arbitrary network request, or free-form command tool. ToolPolicy: who can call what, and how fast? allowed_actions: - "inference:responses:*" - "tool:workspace_get_task:*" - "tool:workspace_read_file:*" - "tool:workspace_search:*" - "tool:workspace_apply_patch:*" - "tool:workspace_run_test:*" - "tool:workspace_get_diff:*" ToolPolicy can also define request rate, burst, time windows, approvals, trust thresholds, and governance profiles. Tool implementation: are valid tools receiving safe arguments? The Workspace MCP rejects: absolute, traversing, or real paths outside the workspace; .env, CI, README, and writes outside src/; non-unique replacement text; oversized files, patches, and diffs; unapproved test IDs; shell-composed commands. Prompt behavior, tool registration, caller authorization, and argument validation are four separate controls. 7. Token limits must be enforced on the request path The tutorial's InferencePolicy uses per-request and daily budgets: spec: tokenBudget: perRequestTokens: 20000 dailyTokens: 100000 When a client requests max_completion_tokens: 20001, the router returns HTTP 429. That is stronger evidence than seeing submitted YAML because it proves that the policy compiled, loaded, and entered the real request path. Later BYO and release examples use tighter limits: modelPreference: primary: provider: azure-openai deployment: gpt-5.6-sol tokenBudget: perRequestTokens: 1024 dailyTokens: 4096 A platform budget cannot determine whether two patches are equivalent or whether a task exceeded a business deadline. The RepairGuard and framework configuration add controls for: duplicate patch digests; excessive repair attempts; task deadlines; maximum MAF iterations and function calls. Token budgets constrain inference cost; repair guards and framework loop limits constrain business failure. 8. From OpenClaw to MAF: change the application, preserve the external boundary OpenClaw is effective for rapidly discovering the conversation, planning, tool, and specialist behavior the product needs. Production requires explicit state, typed tools, repeatable tests, and a human stop. Forge encodes the workflow as application code: class WorkflowState(StrEnum): RECEIVE_REQUIREMENT = "RECEIVE_REQUIREMENT" VALIDATE_SCOPE = "VALIDATE_SCOPE" INSPECT_REPOSITORY = "INSPECT_REPOSITORY" PROPOSE_PLAN = "PROPOSE_PLAN" APPLY_MINIMAL_PATCH = "APPLY_MINIMAL_PATCH" RUN_TARGETED_TESTS = "RUN_TARGETED_TESTS" SUMMARIZE_EVIDENCE = "SUMMARIZE_EVIDENCE" STOP_FOR_HUMAN_REVIEW = "STOP_FOR_HUMAN_REVIEW" There is deliberately no MERGE or DEPLOY state. In the final code/08 path, the kars MAF Python adapter pins the MAF client to the local router before MAF is imported: from kars_runtime_maf_python import bootstrap bootstrap() from agent_framework import Agent, tool from agent_framework.openai import OpenAIChatClient @tool(approval_mode="never_require") def inspect_release_contract(request_id: str, issue_id: str, revision: str) -> str: # Validate the pinned issue and revision, then return bounded evidence. ... maf_client = OpenAIChatClient(model=MODEL) maf_client.function_invocation_configuration["max_iterations"] = 3 maf_client.function_invocation_configuration["max_function_calls"] = 1 builder = Agent( client=maf_client, name="FabrikamReleaseBuilder", tools=[inspect_release_contract], default_options={"store": False}, ) The resulting path is: OpenClaw Intake -> MAF Agent -> inspect_release_contract @tool -> kars MAF Python adapter -> localhost Router -> GitHub Copilot or the selected provider MAF provides the agent, tool, session, middleware, and workflow programming model. kars provides the identity, network, budget, tool, audit, and lifecycle boundary outside the framework. This is one of kars' most important long-term advantages: the application framework can change without rebuilding the complete security shell. 9. Operational security: do not collapse every event into “the network was blocked” The updated code/06 requires incident response to distinguish: HTTPS; DNS; metadata service; local daemon; operator exec; break-glass access. Every denied channel is recorded in a hash-linked audit chain. Break-glass records without an incident ID are also rejected.\ Controller Conditions + Router Denials + Policy Budgets + Admission Decisions + Workload Recovery = One Investigable Sequence Audit integrity is not audit durability The router can verify the current audit hash chain. The tutorial also validates that pod replacement may reset an in-memory audit log, after which a new valid chain begins. Production deployments must continuously export audit data to an independently controlled durable backend. Otherwise, they can prove that the current chain is intact but cannot prove that pre-incident history survived pod loss. 10. AKS and multi-agent release: separation of authority, not two prompts chatting The AKS promotion separates Forge into Builder and Reviewer roles: Control Builder Reviewer Tokens per request 2048 512 Daily tokens 8192 2048 Tool authority Read, search, patch, test, diff Read diff/evidence and submit a decision Approval mode Never Always Trust threshold 700 800 The Builder may create a patch but cannot approve release. The Reviewer may approve a digest-pinned Builder artifact but cannot change source or approve an artifact created by the Reviewer. The final handoff carries patch, test-evidence, and artifact-manifest digests and still stops for human PR approval. The code/08 final sandbox-escape gate explicitly rejects: self-modified authority; symlink escape; host trust handoff; DNS egress; Builder self-approval; Reviewer source modification; untrusted or expired peer drafts. The release standard therefore becomes: Correct behavior must pass, and credential, network, tool, approval, evidence, and containment boundaries must remain intact. Passing tests alone is not release evidence. 11. What kars does not promise kars strengthens the sandbox, but it does not solve every risk automatically: It does not prove that a generated patch is correct. It does not make untrusted code safe to merge. It cannot protect a credential mistakenly mounted into the agent. Local Docker mode does not become a production boundary. It does not replace tenant RBAC, quotas, image policy, signing, supply-chain controls, or durable audit export. It cannot compensate for a policy that deliberately enables arbitrary shell and unrestricted egress. Confidential isolation does not replace least privilege, tool policy, egress policy, and code review. The sandbox bounds authority and blast radius. Tests, evaluation, independent review, and release policy still determine whether a change is acceptable. 12. An enterprise adoption path with measurable exits Phase 1: define the business and threat contract Specify inputs, outputs, allowed actions, forbidden actions, data boundaries, and the human approval point. Exit: product, platform, and security can all explain the agent's maximum authority. Phase 2: validate one OpenClaw vertical slice Use narrow business MCP tools instead of a general shell, and include hostile repository content. Exit: the normal task succeeds while self-configuration, path/symlink, trust-handoff, and egress tests fail. Phase 3: encode the sandbox as a Kubernetes contract Validate UID separation, root filesystem, capabilities, volumes, service-account tokens, NetworkPolicy, egress guard, and exec admission. Exit: the five boundaries are supported by runtime evidence, not only YAML review. Phase 4: add tool, model, and cost governance Apply McpServer, ToolPolicy, and InferencePolicy. Test unknown tools, dangerous arguments, and token overflow. Exit: violations are denied on the live request path. Phase 5: migrate into explicit MAF code Encode workflow state, typed tools, loop limits, evidence, failure paths, and the human stop. Exit: the MAF runtime preserves the external boundary already proven around the OpenClaw prototype. Phase 6: promote to AKS through GitOps Pin source revision, image digest, and loaded policy digest. Separate Builder and Reviewer authority. Prepare the kill switch, rollback, and durable audit export. Exit: one allowed workflow succeeds, multiple escape and authority-violation scenarios are denied, and all results have correlated evidence. Conclusion: kars does not make the model smarter; it makes agent authority explainable Enterprises will ultimately ask: What can the agent access? Where are the provider credentials? Who defines and changes the tool authority? Can prompt injection move data through DNS, metadata, a daemon, or HTTPS? How many tokens and repair iterations may one task consume? Who may patch, approve, merge, or deploy? Does evidence survive pod loss? If OpenClaw is replaced by MAF, does the security model remain intact? The ByteCraft AI story does not argue for one universal agent framework. It argues for a stable Agent Infrastructure layer: Use OpenClaw to discover valuable behavior quickly, use Microsoft Agent Framework to encode that behavior as explicit and testable application code, and use kars to remove credentials, networking, tools, budgets, sandboxing, audit, and lifecycle authority from the agent application itself. An agent becomes an enterprise workload when it has an independent identity boundary, a budget, a constrained tool surface, controlled egress, exportable evidence, and operational suspension and rollback—not merely when it runs inside a container. References Let's Learn Microsoft kars Microsoft kars🚀 Foundry Toolkit for VS Code — August 2026 Update
This is the August round-up for the Foundry Toolkit for VS Code. Four releases shipped this month: 1.6.7, 1.6.8, 1.6.9, and 1.6.10. August was about turning agent development into a workflow you can follow end to end — start from the right path, connect reusable tools and other agents, run with real user isolation, and inspect exactly where the time and tokens went. Have feedback or hit a bug? File an issue on GitHub — the roadmap moves on what you tell us. Highlights Prompt Agent toolboxes — attach a centrally managed toolbox, inspect its tools and skills, manage versions and approval policies, and configure nested tools without leaving Agent Builder. 1.6.10 Agent-to-Agent connections (preview) — connect an Agent2Agent (A2A)-compatible agent from a configured connection, the Foundry account catalog, or a custom HTTPS endpoint. 1.6.10 Agent Inspector Overview — read a latency waterfall and an ordered timeline of model, reasoning, and tool activity for all runs or one selected run. 1.6.9 User-scoped Hosted Agent sessions — set a user identity so Responses conversations and session files stay isolated per user. 1.6.8 A clearer Create Agent start — choose Microsoft Agent Framework, Copilot SDK, LangGraph, Copilot-assisted coding, Agent Builder, or the full sample catalog from one redesigned page. 1.6.9 🤖 Create Agents — start on the right path, then stay in context Starting an agent shouldn't begin with choosing the wrong abstraction. The redesigned Create Agent page gives you direct routes to Microsoft Agent Framework, Copilot SDK, and LangGraph samples, Copilot-assisted coding, Agent Builder, and the complete sample catalog. You decide whether you want code, a guided build, or a prompt agent first — not after scaffolding the wrong project. 1.6.9 Hosted Agent setup is also less brittle. You can choose Skip for now during model setup even when existing deployments fail to load, then wire the model connection later. Administrator-connected Foundry models now appear alongside regular deployments in playgrounds and Hosted Agent creation, so the models your organization already configured are available where you build. 1.6.8 1.6.9 Once an agent is running, identity matters. The Hosted Agent Playground can now set a user identity for Responses conversations, keeping conversation state and session files isolated for each user instead of blending everyone into one test session. And when somebody sends you a Microsoft Foundry portal link, deep links can open that named Hosted Agent's Details or Optimization page directly in VS Code — not the portal home, not a search screen. 1.6.8 1.6.10 🔧 Toolboxes and A2A — connect capabilities once, reuse them An agent with five tools can become five separate configurations, five approval stories, and five places to make the same update. Toolbox changes that shape: it packages centrally managed tools behind one Model Context Protocol (MCP)-compatible endpoint, with shared versioning and policy controls. In August, Prompt Agents gained toolbox workflows inside Agent Builder. Open Add tools to browse toolboxes, or use Add to Prompt Agent from the Toolbox resource list. The attached toolbox appears as a collapsible card where you can inspect tools and skills, switch versions, configure approval policies and nested tools, replace or remove the toolbox, or opt out. You manage the collection — not a loose pile of one-off connections. 1.6.10 Agent-to-agent composition arrives in the same flow. Agent-to-Agent connections (preview) let you add an A2A-compatible agent from an existing connection, the Foundry account catalog, or a custom HTTPS endpoint. Attach it directly to a Prompt Agent or put it inside a toolbox for reuse across agents and runtimes. Your pipeline can now be agent → toolbox → specialist agent — with the connection managed as a real resource instead of buried in prompt text. 1.6.10 🔍 Agent Inspector — see the run, not just the answer A final answer can look right while the run behind it is slow, expensive, or calling the wrong tool. Agent Inspector now gives you the sequence and the evidence. The new default Overview tab shows every run or one selected run through two synchronized views: a latency waterfall and an ordered timeline of model, reasoning, and tool activity. Response footers add the model, duration, total tokens, and timestamp; hover over the token total to split input from output. Raw reasoning and reasoning summaries appear in separate collapsible sections when the agent provides them. 1.6.9 Tool inspection goes deeper in 1.6.10. Calls are grouped by response run, with status, call ID, arguments, and results, and each Responses event can show when it reached Agent Inspector. The Overview waterfall and timeline now scroll independently, while long streaming responses and Details views update more smoothly. You can move from "the tool failed" to the exact call and payload without reconstructing the run from chat bubbles. 1.6.10 The conversation itself is easier to drive: press Up or Down to recall and edit earlier requests without losing your unsent draft, or choose Clear Chat to reset the conversation plus Events and Details state. Pending MCP approvals and OAuth consent requests stay pinned above the input, with bulk actions and expandable details, until every decision is resolved. 1.6.7 1.6.9 🎯 Models and resources — faster to open, steadier when you return Resource pages should remember your work, not reset it. Models and Tools now load the selected tab first and show core rows before fetching the extra details. When you return to Agents, Models, Tools, Knowledge, or Evaluations, the toolkit preserves rows, search, filters, and pagination while refreshing the active view in the background. A manual refresh still gets the latest service state when you ask for it. 1.6.7 The sidebar does less work too. Collapsed My Resources sections load only when you open them, while Search and Recent Agents remain available. Evaluations, Routines, Tools, Skills, and Toolboxes now share consistent loading feedback, and a direct link to Tools or Skills opens the requested tab without loading Toolboxes first. The result isn't a new destination — it's less waiting on the way there. 1.6.7 1.6.8 Model deployment guidance got one sharp fix as well: quota errors now open the token quota page for your current Foundry project, so the recovery path lands on the project that actually needs capacity. 1.6.10 💻 Activity protocol agents — debugging that matches the agent Activity Protocol agents target Microsoft 365 channels, so local debugging should speak the same language. Newly scaffolded Python projects now open Microsoft 365 Agents Playground inside VS Code for local debugging. You stay in the editor and test the activity-shaped conversation before deployment instead of forcing it through an incompatible playground. 1.6.7 Copilot-assisted creation also follows the current Hosted Agent path: current Foundry project and model setup, a managed Python environment, workspace-root debugging, and the latest local run and deployment flow. When you reuse the selected Foundry project, Copilot no longer asks you to choose its Azure location again. 1.6.10 🪲 Fixes and polish Agent Inspector — streamed response and reasoning text stays complete; response text, reasoning, tool calls, and permission decisions keep their original order; replacement turns reject obsolete stream events; and unmatched tool calls or results no longer appear in Details. 1.6.9 1.6.10 Approvals and consent — human-in-the-loop pauses no longer duplicate tool or approval cards, Clear Chat remains available while a turn waits, and continuation responses retain pending approvals until every request is resolved. 1.6.8 1.6.9 Activity Protocol deployment — Azure Bot settings are validated before submission, compatible Bots are reused, identity and application ID conflicts get recovery guidance, and successful deployments no longer open an unsupported Agent Playground. 1.6.7 1.6.8 Agent Builder and MCP OAuth — reopening a Foundry Prompt Agent preserves its selected version and tool configuration, while authorization callbacks complete only the matching connection request. 1.6.8 Accessibility — screen readers announce Model Catalog actions, collapsible Agent Builder and Model Preference controls, and project and model fields with their labels and state; prompt placeholders also meet minimum contrast requirements. 1.6.10 ⚠️ Breaking change and migration GitHub Models has been removed from the Model Catalog, playground, model comparison, Agent Builder, and evaluations following the service's retirement. If a saved workflow or evaluation references GitHub Models, open it and select another available model before running it again. 1.6.7 🚀 Get it and tell us what to build next August connected the whole agent loop: choose the right starting point, reuse governed tools, compose agents through A2A, isolate real users, and inspect the run down to timing, tokens, arguments, and results. Install or update from the Visual Studio Code Marketplace. Read the docs — Foundry Toolkit for Visual Studio Code and the Microsoft Foundry documentation. Explore samples in the Microsoft Foundry samples repository. Browse the full changelog in WHATS_NEW.md. File issues and feature requests at github.com/microsoft/foundry-toolkit/issues. Join the Microsoft Foundry community on Discord. Try a toolbox with your next Prompt Agent, open the run in Agent Inspector, and tell us where the workflow still slows you down. Happy building. 🚀Distributing Agents to Microsoft Teams and Microsoft 365 Copilot Part 4/5
This is the fourth post in our series on the Microsoft agent platform. We cover the Distribute in M365 pillar — publishing your agents to Microsoft Teams and Microsoft 365 Copilot so they reach users where they already work. All examples reference the FibreOps repository, demonstrated at Microsoft Build BRK241. The Distribution Story Building a great agent is only half the challenge. The other half is getting it into the hands of users without asking them to learn a new tool, visit a new URL, or change their workflow. Microsoft 365 Copilot and Microsoft Teams are where enterprise users already spend their day, making them the natural distribution surface for agents. With the GA release, publishing an agent to Teams and M365 Copilot is a single command. No separate app registration portal, no manual manifest assembly, no multi-step approval workflow for development and testing. Publishing to Microsoft 365 Copilot (GA) FibreOps ships as a declarative agent + action plugin ready for sideload. A single CLI command produces the complete package: python -m fibreops.demo publish-m365 --out dist/m365 # Output: # ✓ wrote dist/m365/declarativeAgent.json # ✓ wrote dist/m365/fibreops-action.json # ✓ wrote dist/m365/manifest.json # ✓ wrote dist/m365/color.png (192x192) # ✓ wrote dist/m365/outline.png ( 32x32) # ✓ wrote dist/m365/fibreops-copilot.zip What Gets Generated File Purpose declarativeAgent.json Defines the agent's persona, capabilities, and conversation starters for M365 Copilot fibreops-action.json Action plugin that proxies tool calls to the deployed FastAPI backend via OpenAPI manifest.json Teams app manifest with publisher metadata, permissions, and capabilities color.png / outline.png App icons for Teams and M365 surfaces fibreops-copilot.zip Ready-to-upload package for Teams Admin Center Configuration Set the base URL to your deployed FastAPI app before publishing — the action plugin uses this to resolve the OpenAPI runtime: # Set the public HTTPS hostname of the deployed FastAPI app $env:M365_ACTION_BASE_URL = "https://fibreops-demo.azurewebsites.net" # Optional: customise publisher metadata $env:M365_PUBLISHER_NAME = "Contoso Network Operations" $env:M365_PUBLISHER_WEBSITE = "https://contoso.com/noc" # Generate the package python -m fibreops.demo publish-m365 --out dist/m365 Environment Variable Purpose M365_ACTION_BASE_URL Public HTTPS root for the FastAPI /openapi.json (e.g., Container Apps FQDN) M365_APP_ID Override the generated Teams app GUID (default: deterministic per repo) M365_PUBLISHER_NAME Publisher name shown in M365 Admin Center M365_PUBLISHER_WEBSITE Publisher website link Uploading the Package Upload the generated fibreops-copilot.zip through either path: Teams Admin Center → Manage apps → Upload new app M365 Admin Center → Integrated apps → Upload custom apps Once uploaded, the declarative agent: Inherits the publisher metadata you configured Advertises conversation starters from the FibreOps deck (e.g., "What is the current outage status?", "Dispatch an engineer to FN-LDN-001") Proxies tool calls to the deployed FastAPI app via the action plugin Appears in Microsoft 365 Copilot as a specialised agent users can invoke How Declarative Agents Work A declarative agent in Microsoft 365 Copilot is defined by metadata rather than code running in the M365 surface. The intelligence lives in your backend — Copilot handles the conversational UX, tool orchestration schema, and user authentication. The flow: User invokes the agent in Microsoft 365 Copilot or Teams Copilot renders conversation starters and accepts natural language input When the agent needs to act, Copilot calls the action plugin (your OpenAPI endpoint) Your FastAPI backend processes the request using the full agent pipeline Results return to the user in the Copilot/Teams UX This architecture means your agent logic stays in one place — the backend. The M365 surface is purely a distribution and interaction layer. Action Plugins and OpenAPI The action plugin ( fibreops-action.json ) references your FastAPI app's /openapi.json endpoint. FibreOps exposes a JSON API that the action plugin can call: /api/runs — List and query agent runs /api/optimiser — Get optimizer scores and suggestions /sdk/chat — Natural language interaction with the agent system /healthz — Liveness probe Because FastAPI auto-generates OpenAPI schemas from your typed Python endpoints, the action plugin gets accurate parameter descriptions, response schemas, and error codes without any manual specification work. Publishing as Autopilots (Public Preview) Autopilots take distribution one step further — agents that operate autonomously without requiring a user to initiate each interaction. An Autopilot can: React to events (e.g., a critical telemetry signal) without human initiation Take actions within defined guardrails Notify users only when human intervention is needed Operate continuously across Microsoft 365 surfaces For FibreOps, an Autopilot would monitor the Event Hub stream continuously and only surface to the NOC team when an incident exceeds automated resolution capability — a fully autonomous operations agent. Teams Adaptive Cards FibreOps posts rich Adaptive Card notifications to Microsoft Teams throughout the agent pipeline. This is separate from the declarative agent — it is a push notification channel for real-time operational awareness. # The NetOps agent posts an outage notice via Incoming Webhook def post_outage_notice(incident_id, node_id, severity, summary, engineer=None): card = { "type": "AdaptiveCard", "body": [ {"type": "TextBlock", "text": f"🚨 Outage: {node_id}", "weight": "Bolder", "size": "Large"}, {"type": "FactSet", "facts": [ {"title": "Severity", "value": severity.upper()}, {"title": "Incident", "value": incident_id}, {"title": "Summary", "value": summary}, ]}, ], "actions": [ {"type": "Action.OpenUrl", "title": "View in NOC Console", "url": f"{base_url}/runs/{incident_id}"} ] } # POST to Teams webhook or append to outbox for offline mode ... If TEAMS_WEBHOOK_URL is not configured, cards are appended to state/teams_outbox.jsonl for review in the NOC console's Teams panel. End-to-End: From Code to Copilot Here is the complete flow from development to distribution: Build — Develop agents with Microsoft Agent Framework, test locally with python -m fibreops.demo --backend local Publish agents — python -m fibreops.demo publish creates hosted Prompt Agents in Foundry Deploy infrastructure — azd up provisions App Service, ACR, Event Hub, Key Vault, and Application Insights Deploy hosted agent — azd env set FIBREOPS_DEPLOY_HOSTED true && azd up Generate M365 package — python -m fibreops.demo publish-m365 --out dist/m365 Upload to Teams — Upload fibreops-copilot.zip via Teams Admin Center Users interact — The agent is now available in Microsoft 365 Copilot and Teams Security Considerations Managed Identity — The deployed app uses system-assigned managed identity for all Azure service access. No secrets in code. Least privilege — Each role grant is scoped to the minimum required (Event Hubs Data Owner, Key Vault Secrets User, AcrPull, Azure AI Developer). Authentication — The M365 Copilot surface handles user authentication; your backend receives authenticated requests. Guardrails — Autopilots operate within defined boundaries; human-in-the-loop escalation is built into the Routine and agent decision logic. Key Takeaways Publishing to Teams and M365 Copilot is GA — a single command generates the complete package. Declarative agents separate distribution (M365) from intelligence (your backend). Action plugins leverage your existing FastAPI OpenAPI schema — no manual specification needed. Autopilots (Public Preview) enable fully autonomous operation within guardrails. Adaptive Cards provide real-time push notifications alongside the conversational agent surface. The same backend serves the NOC console, the Copilot SDK, and the M365 declarative agent. Next Steps Explore the FibreOps repository — try python -m fibreops.demo publish-m365 Microsoft 365 Copilot extensibility documentation Next in this series: Voice Live and Observability for Production Agent SystemsBuilding HIPAA-Compliant Medical Transcription with Local AI
Building HIPAA-Compliant Medical Transcription with Local AI Introduction Healthcare organizations generate vast amounts of spoken content, patient consultations, research interviews, clinical notes, medical conferences. Transcribing these recordings traditionally requires either manual typing (time-consuming and expensive) or cloud transcription services (creating immediate HIPAA compliance concerns). Every audio file sent to external APIs exposes Protected Health Information (PHI), requires Business Associate Agreements, creates audit trails on third-party servers, and introduces potential breach vectors. This sample solution lies in on-premises voice-to-text systems that process audio entirely locally, never sending PHI beyond organizational boundaries. This article demonstrates building a sample medical transcription application using FLWhisper, ASP.NET Core, C#, and Microsoft Foundry Local with OpenAI Whisper models. You'll learn how to build sample HIPAA-compliant audio processing, integrate Whisper models for medical terminology accuracy, design privacy-first API patterns, and build responsive web UIs for healthcare workflows. Whether you're developing electronic health record (EHR) integrations, building clinical research platforms, or implementing dictation systems for medical practices, this sample could be a great starting point for privacy-first speech recognition. Why Local Transcription Is Critical for Healthcare Healthcare data handling is fundamentally different from general business data due to HIPAA regulations, state privacy laws, and professional ethics obligations. Understanding these requirements explains why cloud transcription services, despite their convenience, create unacceptable risks for medical applications. HIPAA compliance mandates strict controls over PHI. Every system that touches patient data must implement administrative, physical, and technical safeguards. Cloud transcription APIs require Business Associate Agreements (BAAs), but even with paperwork, you're entrusting PHI to external systems. Every API call creates logs on vendor servers, potentially in multiple jurisdictions. Data breaches at transcription vendors expose patient information, creating liability for healthcare organizations. On-premises processing eliminates these third-party risks entirely, PHI never leaves your controlled environment. US State laws increasingly add requirements beyond HIPAA. California's CCPA, New York's SHIELD Act, and similar legislation create additional compliance obligations. International regulations like GDPR prohibit transferring health data outside approved jurisdictions. Local processing simplifies compliance by keeping data within organizational boundaries. Research applications face even stricter requirements. Institutional Review Boards (IRBs) often require explicit consent for data sharing with external parties. Cloud transcription may violate study protocols that promise "no third-party data sharing." Clinical trials in pharmaceutical development handle proprietary information alongside PHI, double jeopardy for data exposure. Local transcription maintains research integrity while enabling audio analysis. Cost considerations favor local deployment at scale. Medical organizations generate substantial audio, thousands of patient encounters monthly. Cloud APIs charge per minute of audio, creating significant recurring costs. Local models have fixed infrastructure costs that scale economically. A modest GPU server can process hundreds of hours monthly at predictable expense. Latency matters for clinical workflows. Doctors and nurses need transcriptions available immediately after patient encounters to review and edit while details are fresh. Cloud APIs introduce network delays, especially problematic in rural health facilities with limited connectivity. Local inference provides <1 second turnaround for typical consultation lengths. Application Architecture: ASP.NET Core with Foundry Local The sample FLWhisper application implements clean separation between audio handling, AI inference, and state management using modern .NET patterns: The ASP.NET Core 10 minimal API provides HTTP endpoints for health checks, audio transcription, and sample file streaming. Minimal APIs reduce boilerplate while maintaining full middleware support for error handling, authentication, and CORS. The API design follows OpenAI's transcription endpoint specification, enabling drop-in replacement for existing integrations. The service layer encapsulates business logic: FoundryModelService manages model loading and lifetime, TranscriptionService handles audio processing and AI inference, and SampleAudioService provides demonstration files for testing. This separation enables easy testing, dependency injection, and service swapping. Foundry Local integration uses the Microsoft.AI.Foundry.Local.WinML SDK. Unlike cloud APIs requiring authentication and network calls, this SDK communicates directly with the local Foundry service via in-process calls. Models load once at startup, remaining resident in memory for sub-second inference on subsequent requests. The static file frontend delivers vanilla HTML/CSS/JavaScript, no framework overhead. This simplicity aids healthcare IT security audits and enables deployment on locked-down hospital networks. The UI provides file upload, sample selection, audio preview, transcription requests, and result display with copy-to-clipboard functionality. Here's the architectural flow for transcription requests: Web UI (Upload Audio File) ↓ POST /v1/audio/transcriptions (Multipart Form Data) ↓ ASP.NET Core API Route ↓ TranscriptionService.TranscribeAudio(audioStream) ↓ Foundry Local Model (Whisper Medium locally) ↓ Text Result + Metadata (language, duration) ↓ Return JSON/Text Response ↓ Display in UI This architecture embodies several healthcare system design principles: Data never leaves the device: All processing occurs on-premises, no external API calls No data persistence by default: Audio and transcripts are session-only, never saved unless explicitly configured Comprehensive health checks: System readiness verification before accepting PHI Audit logging support: Structured logging for compliance documentation Graceful degradation: Clear error messages when models unavailable rather than silent failures Setting Up Foundry Local with Whisper Models Foundry Local supports multiple Whisper model sizes, each with different accuracy/speed tradeoffs. For medical transcription, accuracy is paramount—misheard drug names or dosages create patient safety risks: # Install Foundry Local (Windows) winget install Microsoft.FoundryLocal # Verify installation foundry --version # Download Whisper Medium model (optimal for medical accuracy) foundry model add openai-whisper-medium-generic-cpu:1 # Check model availability foundry model list Whisper Medium (769M parameters) provides the best balance for medical use. Smaller models (Tiny, Base) miss medical terminology frequently. Larger models (Large) offer marginal accuracy gains at 3x inference time. Medium handles medical vocabulary well, drug names, anatomical terms, procedure names, while processing typical consultation audio (5-10 minutes) in under 30 seconds. The application detects and loads the model automatically: // Services/FoundryModelService.cs using Microsoft.AI.Foundry.Local.WinML; public class FoundryModelService { private readonly ILogger _logger; private readonly FoundryOptions _options; private ILocalAIModel? _loadedModel; public FoundryModelService( ILogger logger, IOptions options) { _logger = logger; _options = options.Value; } public async Task InitializeModelAsync() { try { _logger.LogInformation( "Loading Foundry model: {ModelAlias}", _options.ModelAlias ); // Load model from Foundry Local _loadedModel = await FoundryClient.LoadModelAsync( modelAlias: _options.ModelAlias, cancellationToken: CancellationToken.None ); if (_loadedModel == null) { _logger.LogWarning("Model loaded but returned null instance"); return false; } _logger.LogInformation( "Successfully loaded model: {ModelAlias}", _options.ModelAlias ); return true; } catch (Exception ex) { _logger.LogError( ex, "Failed to load Foundry model: {ModelAlias}", _options.ModelAlias ); return false; } } public ILocalAIModel? GetLoadedModel() => _loadedModel; public async Task UnloadModelAsync() { if (_loadedModel != null) { await FoundryClient.UnloadModelAsync(_loadedModel); _loadedModel = null; _logger.LogInformation("Model unloaded"); } } } Configuration lives in appsettings.json , enabling easy customization without code changes: { "Foundry": { "ModelAlias": "whisper-medium", "LogLevel": "Information" }, "Transcription": { "MaxAudioDurationSeconds": 300, "SupportedFormats": ["wav", "mp3", "m4a", "flac"], "DefaultLanguage": "en" } } Implementing Privacy-First Transcription Service The transcription service handles audio processing while maintaining strict privacy controls. No audio or transcript persists beyond the HTTP request lifecycle unless explicitly configured: // Services/TranscriptionService.cs public class TranscriptionService { private readonly FoundryModelService _modelService; private readonly ILogger _logger; public async Task TranscribeAudioAsync( Stream audioStream, string originalFileName, TranscriptionOptions? options = null) { options ??= new TranscriptionOptions(); var startTime = DateTime.UtcNow; try { // Validate audio format ValidateAudioFormat(originalFileName); // Get loaded model var model = _modelService.GetLoadedModel(); if (model == null) { throw new InvalidOperationException("Whisper model not loaded"); } // Create temporary file (automatically deleted after transcription) using var tempFile = new TempAudioFile(audioStream); // Execute transcription _logger.LogInformation( "Starting transcription for file: {FileName}", originalFileName ); var transcription = await model.TranscribeAsync( audioFilePath: tempFile.Path, language: options.Language, cancellationToken: CancellationToken.None ); var duration = (DateTime.UtcNow - startTime).TotalSeconds; _logger.LogInformation( "Transcription completed in {Duration:F2}s", duration ); return new TranscriptionResult { Text = transcription.Text, Language = transcription.Language ?? options.Language, Duration = transcription.AudioDuration, ProcessingTimeSeconds = duration, FileName = originalFileName, Timestamp = DateTime.UtcNow }; } catch (Exception ex) { _logger.LogError( ex, "Transcription failed for file: {FileName}", originalFileName ); throw; } } private void ValidateAudioFormat(string fileName) { var extension = Path.GetExtension(fileName).TrimStart('.'); var supportedFormats = new[] { "wav", "mp3", "m4a", "flac", "ogg" }; if (!supportedFormats.Contains(extension.ToLowerInvariant())) { throw new ArgumentException( $"Unsupported audio format: {extension}. " + $"Supported: {string.Join(", ", supportedFormats)}" ); } } } // Temporary file wrapper that auto-deletes internal class TempAudioFile : IDisposable { public string Path { get; } public TempAudioFile(Stream sourceStream) { Path = System.IO.Path.GetTempFileName(); using var fileStream = File.OpenWrite(Path); sourceStream.CopyTo(fileStream); } public void Dispose() { try { if (File.Exists(Path)) { File.Delete(Path); } } catch { // Ignore deletion errors in temp folder } } } This service demonstrates several privacy-first patterns: Temporary file lifecycle management: Audio written to temp storage, automatically deleted after transcription No implicit persistence: Results returned to caller, not saved by service Format validation: Accept only supported audio formats to prevent processing errors Comprehensive logging: Audit trail for compliance without logging PHI content Error isolation: Exceptions contain diagnostic info but no patient data Building the OpenAI-Compatible REST API The API endpoint mirrors OpenAI's transcription API specification, enabling existing integrations to work without modifications: // Program.cs var builder = WebApplication.CreateBuilder(args); // Configure services builder.Services.Configure( builder.Configuration.GetSection("Foundry") ); builder.Services.AddSingleton(); builder.Services.AddScoped(); builder.Services.AddHealthChecks() .AddCheck("foundry-health"); var app = builder.Build(); // Load model at startup var modelService = app.Services.GetRequiredService(); await modelService.InitializeModelAsync(); app.UseHealthChecks("/health"); app.MapHealthChecks("/api/health/status"); // OpenAI-compatible transcription endpoint app.MapPost("/v1/audio/transcriptions", async ( HttpRequest request, TranscriptionService transcriptionService, ILogger logger) => { if (!request.HasFormContentType) { return Results.BadRequest(new { error = "Content-Type must be multipart/form-data" }); } var form = await request.ReadFormAsync(); // Extract audio file var audioFile = form.Files.GetFile("file"); if (audioFile == null || audioFile.Length == 0) { return Results.BadRequest(new { error = "Audio file required in 'file' field" }); } // Parse options var format = form["format"].ToString() ?? "text"; var language = form["language"].ToString() ?? "en"; try { // Process transcription using var stream = audioFile.OpenReadStream(); var result = await transcriptionService.TranscribeAudioAsync( audioStream: stream, originalFileName: audioFile.FileName, options: new TranscriptionOptions { Language = language } ); // Return in requested format if (format == "json") { return Results.Json(new { text = result.Text, language = result.Language, duration = result.Duration }); } else { // Default: plain text return Results.Text(result.Text); } } catch (Exception ex) { logger.LogError(ex, "Transcription request failed"); return Results.StatusCode(500); } }) .DisableAntiforgery() // File uploads need CSRF exemption .WithName("TranscribeAudio") .WithOpenApi(); app.Run(); Example API usage: # PowerShell $audioFile = Get-Item "consultation-recording.wav" $response = Invoke-RestMethod ` -Uri "http://localhost:5192/v1/audio/transcriptions" ` -Method Post ` -Form @{ file = $audioFile; format = "json" } Write-Output $response.text # cURL curl -X POST http://localhost:5192/v1/audio/transcriptions \ -F "file=@consultation-recording.wav" \ -F "format=json" Building the Interactive Web Frontend The web UI provides a user-friendly interface for non-technical medical staff to transcribe recordings: SarahCare Medical Transcription The JavaScript handles file uploads and API interactions: // wwwroot/app.js let selectedFile = null; async function checkHealth() { try { const response = await fetch('/health'); const statusEl = document.getElementById('status'); if (response.ok) { statusEl.className = 'status-badge online'; statusEl.textContent = '✓ System Ready'; } else { statusEl.className = 'status-badge offline'; statusEl.textContent = '✗ System Unavailable'; } } catch (error) { console.error('Health check failed:', error); } } function handleFileSelect(event) { const file = event.target.files[0]; if (!file) return; selectedFile = file; // Show file info const fileInfo = document.getElementById('fileInfo'); fileInfo.textContent = `Selected: ${file.name} (${formatFileSize(file.size)})`; fileInfo.classList.remove('hidden'); // Enable audio preview const preview = document.getElementById('audioPreview'); preview.src = URL.createObjectURL(file); preview.classList.remove('hidden'); // Enable transcribe button document.getElementById('transcribeBtn').disabled = false; } async function transcribeAudio() { if (!selectedFile) return; const loadingEl = document.getElementById('loadingIndicator'); const resultEl = document.getElementById('resultSection'); const transcribeBtn = document.getElementById('transcribeBtn'); // Show loading state loadingEl.classList.remove('hidden'); resultEl.classList.add('hidden'); transcribeBtn.disabled = true; try { const formData = new FormData(); formData.append('file', selectedFile); formData.append('format', 'json'); const startTime = Date.now(); const response = await fetch('/v1/audio/transcriptions', { method: 'POST', body: formData }); if (!response.ok) { throw new Error(`HTTP ${response.status}: ${response.statusText}`); } const result = await response.json(); const processingTime = ((Date.now() - startTime) / 1000).toFixed(1); // Display results document.getElementById('transcriptionText').value = result.text; document.getElementById('resultDuration').textContent = `Duration: ${result.duration.toFixed(1)}s`; document.getElementById('resultLanguage').textContent = `Language: ${result.language}`; resultEl.classList.remove('hidden'); console.log(`Transcription completed in ${processingTime}s`); } catch (error) { console.error('Transcription failed:', error); alert(`Transcription failed: ${error.message}`); } finally { loadingEl.classList.add('hidden'); transcribeBtn.disabled = false; } } function copyToClipboard() { const text = document.getElementById('transcriptionText').value; navigator.clipboard.writeText(text) .then(() => alert('Copied to clipboard')) .catch(err => console.error('Copy failed:', err)); } // Initialize window.addEventListener('load', () => { checkHealth(); loadSamplesList(); }); Key Takeaways and Production Considerations Building HIPAA-compliant voice-to-text systems requires architectural decisions that prioritize data privacy over convenience. The FLWhisper application demonstrates that you can achieve accurate medical transcription, fast processing times, and intuitive user experiences entirely on-premises. Critical lessons for healthcare AI: Privacy by architecture: Design systems where PHI never exists outside controlled environments, not as a configuration option No persistence by default: Audio and transcripts should be ephemeral unless explicitly saved with proper access controls Model selection matters: Whisper Medium provides medical terminology accuracy that smaller models miss Health checks enable reliability: Systems should verify model availability before accepting PHI Audit logging without content logging: Track operations for compliance without storing sensitive data in logs For production deployment in clinical settings, integrate with EHR systems via HL7/FHIR interfaces. Implement role-based access control with Active Directory integration. Add digital signatures for transcript authentication. Configure automatic PHI redaction using clinical NLP models. Deploy on HIPAA-compliant infrastructure with proper physical security. Implement comprehensive audit logging meeting compliance requirements. The complete implementation with ASP.NET Core API, Foundry Local integration, sample audio files, and comprehensive tests is available at github.com/leestott/FLWhisper. Clone the repository and follow the setup guide to experience privacy-first medical transcription. Resources and Further Reading FLWhisper Repository - Complete C# implementation with .NET 10 Quick Start Guide - Installation and usage instructions Microsoft Foundry Local Documentation - SDK reference and model catalog OpenAI Whisper Documentation - Model architecture and capabilities HIPAA Compliance Guidelines - HHS official guidance Testing Guide - Comprehensive test suite documentationBuilding Autonomous Agents with Microsoft Agent Framework and GitHub Copilot SDK Part 2/5
This is the second post in our series on the Microsoft agent platform. Here we dive deep into building autonomous agents, the development experience, the Microsoft Agent Framework, tool design patterns, and how the GitHub Copilot SDK brings conversational AI to your agent system. All examples reference the FibreOps repository, an autonomous fibre outage response system demonstrated at Microsoft Build BRK241. The Microsoft Agent Framework The Microsoft Agent Framework (now GA) provides a unified programming model for building agents. It supports multiple backends through a single .run() contract: Hosted — FoundryAgent connected to a Prompt Agent published to Microsoft Foundry Agent Service. Foundry — Agent + FoundryChatClient with the definition resolved locally (ideal for prompt iteration). Local — Deterministic LocalAgent for offline development and testing. This design means your orchestration code never changes regardless of where the agent runs. The factory pattern in FibreOps selects the backend at startup: # src/fibreops/agents/factory.py — simplified from agent_framework_foundry import FoundryAgent from agent_framework import Agent, FoundryChatClient def build_agent(role: str, backend: str, config: Config): if backend == "hosted": return FoundryAgent(agent_id=config.foundry_agents[role]) elif backend == "foundry": return Agent( instructions=get_instructions(role), chat_client=FoundryChatClient(endpoint=config.endpoint), tools=get_tools(role), ) else: return LocalAgent(role=role) Set FIBREOPS_AGENT_BACKEND to override the backend, or leave it as auto for intelligent detection. Designing Role-Specialised Agents FibreOps demonstrates a key pattern: role specialisation. Rather than one monolithic agent, the system uses three focused agents, each with a clear responsibility boundary: Agent Role Tools Available IncidentAnalysisAgent Classify severity, find root cause, retrieve SOP Knowledge (SOPs + topology), Web IQ, Work IQ NetOpsCoordinatorAgent File D365 incident, post Teams notice Ticketing, Teams, Memory FieldDispatchAgent Select engineer, book resource, update team Dispatch, Teams, Voice Why Role Specialisation? Focused system prompts — Each agent has a tightly scoped instruction set, reducing hallucination and improving reliability. Independent evaluation — You can score each agent separately against role-specific criteria. Parallel development — Teams can iterate on agents independently. Selective upgrade — Swap one agent's model or implementation without touching others. Tool Design: Typed Python Functions Tools in the Microsoft Agent Framework are typed Python functions that the runtime supplies to the hosted agent definition. FibreOps demonstrates several tool categories: Knowledge Tools # src/fibreops/tools/knowledge.py — simplified def sop_lookup(node_id: str, signal_type: str) -> dict: """Retrieve the Standard Operating Procedure for a given signal type. Args: node_id: The fibre node identifier (e.g., FN-LDN-001) signal_type: The type of signal (loss_of_light, high_ber, signal_degradation) Returns: SOP with steps, escalation path, and estimated resolution time. """ # Load from local markdown SOPs or Foundry IQ ... def web_iq_search(query: str, *, limit: int = 5) -> list[dict]: """Search public web for context relevant to the incident. Grounding against roadworks, weather, power outages, splice guidance. Falls back to deterministic fixtures when endpoint is unset. """ ... def work_iq_search(query: str, *, limit: int = 5) -> list[dict]: """Search enterprise knowledge for context relevant to the incident. Site surveys, SLA tiers, competency matrix, MTTR trends. """ ... Integration Tools # src/fibreops/tools/teams.py — simplified def post_outage_notice( incident_id: str, node_id: str, severity: str, summary: str, engineer: str | None = None, ) -> dict: """Post an Adaptive Card outage notice to the configured Teams channel. If TEAMS_WEBHOOK_URL is not set, appends to state/teams_outbox.jsonl for offline review. """ card = build_adaptive_card(incident_id, node_id, severity, summary, engineer) if config.teams_webhook_url: requests.post(config.teams_webhook_url, json=card) else: append_to_outbox(card) return {"status": "posted", "incident_id": incident_id} Design Principles for Agent Tools Typed parameters with docstrings — The runtime uses type hints and docstrings to generate the tool schema for the LLM. Graceful degradation — Every tool works offline by falling back to local fixtures or file-based state. Idempotent where possible — Tools that create resources return existing records if called with the same parameters. Observable — Every tool invocation emits an OpenTelemetry span for tracing and debugging. The Orchestrator Pattern The orchestrator drives signals through the agent pipeline. It is deliberately simple — a linear flow with error handling: # src/fibreops/orchestrator.py — simplified async def handle_signal(signal: TelemetrySignal) -> RunResult: """Process a telemetry signal through the agent pipeline.""" # Stage 1: Incident Analysis analysis = await incident_agent.run( f"Analyse this signal: {signal.model_dump_json()}" ) # Stage 2: NetOps Coordination coordination = await netops_agent.run( f"Coordinate response for: {analysis.summary}" ) # Stage 3: Field Dispatch dispatch = await dispatch_agent.run( f"Dispatch engineer for incident: {coordination.incident_id}" ) return RunResult( signal=signal, analysis=analysis, coordination=coordination, dispatch=dispatch, ) The orchestrator honours the same contract regardless of backend — hosted , foundry , or local — because all backends implement await agent.run(prompt) . GitHub Copilot SDK Integration (GA) The GitHub Copilot SDK enables conversational interaction with your agent system. FibreOps implements FibreOpsCopilotClient with the same interface as github/copilot-sdk : # src/fibreops/sdk/__init__.py — simplified from fibreops.sdk.client import FibreOpsCopilotClient client = FibreOpsCopilotClient() session = client.create_session() # Query agent status response = session.send_and_wait("status") print(response.text) # Human-readable summary print(response.data) # Structured JSON # Inject a telemetry signal via conversation response = session.send_and_wait(json.dumps({ "signal_id": "sig-demo", "node_id": "FN-LDN-001", "signal_type": "loss_of_light", "severity": "critical" })) The adapter routes prompts by shape: JSON signal-shaped dicts — Forwarded to the orchestrator for processing. Free-form text — Answered by a deterministic responder ( help , status , nodes , engineers , optimiser , dispatch ). Drive it from the terminal: python -m fibreops.demo chat "help" python -m fibreops.demo chat "status" python -m fibreops.demo chat '{"signal_id":"sig-demo","node_id":"FN-LDN-001","signal_type":"loss_of_light","severity":"critical"}' Or hit the embedded HTTP endpoint when the NOC console is running: Invoke-RestMethod -Method Post http://127.0.0.1:8800/sdk/chat -Body '{"prompt":"status"}' -ContentType application/json Development Workflow with Foundry Toolkit for VS Code The Foundry Toolkit for VS Code provides an integrated development experience: Author prompts — Edit system instructions with live preview and token counting. Test locally — Run against the foundry backend with FoundryChatClient pointing at your development model. Iterate fast — The foundry backend resolves definitions locally, so prompt changes take effect immediately without republishing. Publish when ready — python -m fibreops.demo publish creates hosted Prompt Agents in Foundry. Multi-Model Support The Microsoft Agent Framework supports multiple models. FibreOps defaults to gpt-4.1-mini (the model available in most demo Foundry accounts), but any chat-completions deployment works: # .env AZURE_AI_MODEL_DEPLOYMENT=gpt-4.1-mini # or gpt-4o-mini, gpt-4o, gpt-4.1 The framework also supports Claude Code connectors and Magentic-One for multi-agent collaboration scenarios. Testing Strategy FibreOps demonstrates a layered testing approach: Unit tests — Test tools in isolation with mocked dependencies. Local backend tests — Run the full pipeline with LocalAgent for deterministic assertions. Integration tests — Run against real Foundry agents with pytest -q . Rubric evaluation — The optimizer scores every run against defined criteria. # Run the test suite .\.venv\Scripts\python.exe -m pytest -q Key Takeaways The Microsoft Agent Framework provides a unified .run() contract across hosted, foundry, and local backends. Role specialisation keeps agents focused, testable, and independently evolvable. Tools are typed Python functions with docstrings — the runtime generates schemas automatically. The GitHub Copilot SDK (GA) enables conversational interaction with any agent system. Graceful degradation means the entire system works offline for development. The factory pattern lets you switch backends without changing orchestration code. Next Steps Clone the FibreOps repository and run python -m fibreops.demo --signals 3 Microsoft Agent Framework documentation Next in this series: Running Hosted Agents in Microsoft Foundry Agent Service
Vector search finds candidates. Reranking decides what your RAG app reads
You ask a retrieval-augmented generation (RAG) application a question. Vector search returns ten passages that are clearly related to the topic. The passage that actually contains the answer, however, is ranked seventh, while the language model receives only the first five. Retrieval did not completely fail. It found the evidence, but ordered it below less useful context. Reranking addresses that gap between a passage that is semantically similar and a passage that is relevant to the user's specific question. This article demonstrates that pattern in four Azure services using the Stanford Question Answering Dataset (SQuAD). The goal is not to declare a winning service or publish a quality benchmark. It is to show where retrieval, rank fusion, and model-based reranking run in each architecture, and to illustrate how the position of a known source passage can change. What this demonstration establishes The examples show rank movement for three selected questions. They do not establish that one reranker or service is universally more accurate. A production decision requires a larger, representative query set and aggregate relevance, latency, and cost measurements. Get the full Python implementation: pauldj54/azure-vector-reranking-squad Retrieval and reranking are different stages A production search pipeline commonly uses two stages: Retrieve for recall. Fast retrieval narrows a large corpus to a bounded candidate set. It can use vector search, keyword search, or both. Rerank for precision. A more expensive model evaluates only those candidates against the original query and produces the final order. Reciprocal Rank Fusion (RRF) belongs between those two ideas. RRF is a model-free rank aggregation method that merges independent result lists, usually vector and keyword results. For a document d, a typical score is: RRF(d) = ∑ r ∈ R 1 k + rank r (d) Here, R is the set of ranked lists and k is commonly 60. RRF works with positions rather than raw scores, so it can combine signals such as cosine distance and BM25 without pretending their score scales are comparable. This gives a clearer three-part vocabulary: Stage Purpose Typical mechanism Retrieve Find broad candidate set Vector search, BM25, filters Fuse Combine independent rankings RRF Rerank Reassess query-document relevance Semantic ranker or cross-encoder RRF often improves hybrid retrieval when exact names, dates, identifiers, or terms matter. A learned reranker can then read the query and each candidate together, capturing interactions that separately generated embeddings can miss. The learned stage costs more, so it should operate on tens of candidates rather than the whole corpus. The following image describes the general process: Why use SQuAD for this demonstration? SQuAD 1.1 contains crowd-written questions over more than 500 Wikipedia articles. Its packaged splits contain 87,599 training rows and 10,570 validation rows. Each row includes a question, a context passage, and one or more answer spans inside that passage. That source-context mapping gives this demonstration a useful label: the context associated with a question is treated as its gold passage. We can then inspect whether each search stage moves that passage up or down. This is convenient, but it is not a perfect passage-ranking benchmark. SQuAD was designed for extractive question answering, and another passage in the corpus might also answer a question. The gold context is therefore a reproducible reference, not proof that every other passage is irrelevant. The results shown here use the 2,067 unique contexts in the SQuAD validation split and 1,536-dimensional embeddings. The repository default should be set to the same corpus size before treating the screenshots or rank transitions as directly reproducible. Three illustrative questions Question Expected answer Gold context According to game stats, which Super Bowl 50 quarterback had his worst year since his first NFL season? Peyton Manning 12, Super Bowl 50 What else did Tesla do for work at this time? Various electrical repair jobs 165, Nikola Tesla Who acts as laborer, paymaster, and design team for a renovation project? The property owner 1306, Construction Each notebook selects a seeded demonstration question when it runs. The three saved examples were collected across separate runs; the current notebooks do not execute all three questions in one pass. A benchmark harness should iterate over a fixed question list and save all stage results in one structured output. Capability boundaries at a glance Service Retrieval and Fusion Learned Reranking Boundary to Keep in Mind Azure AI Search Native keyword and vector retrieval with native RRF Built-in semantic ranker Semantic ranking only reorders the retrieved top 50 Azure SQL Database Exact vector retrieval in the current notebook External Cohere model invoked through native REST procedure SQL issues the HTTPS request; Foundry performs inference PostgreSQL Flexible Server pgvector plus hand-written SQL RRF over full-text search Optional external Cohere call from Python Retrieval primitives are native; this RRF query and Cohere path are application code Azure Cosmos DB for NoSQL Native vector search and native hybrid RRF SDK-integrated Semantic Reranker, currently preview Reranking is a separate inference call over at most 50 supplied documents Azure AI Search: native hybrid retrieval and semantic ranking How it works: Azure AI Search provides the most integrated pipeline in this demonstration. A hybrid query runs keyword and vector retrieval, combines the lists with RRF, and passes up to the top 50 results to the built-in semantic ranker. The semantic ranker assigns @search.rerankerScore values from 0 to 4 and can return extractive captions and answers. The semantic configuration identifies the fields that carry the meaning of each document: semantic_search = SemanticSearch( configurations=[ SemanticConfiguration( name=SEMANTIC_CONFIG, prioritized_fields=SemanticPrioritizedFields( title_field=SemanticField(field_name="title"), content_fields=[SemanticField(field_name="content")], ), ) ] ) This tells the semantic ranker which text fields to evaluate. The query then enables semantic ranking after hybrid retrieval: results = search_client.search( search_text=question, vector_queries=[vector_query], query_type="semantic", semantic_configuration_name=SEMANTIC_CONFIG, top=10, ) The important constraint is candidate recall. Semantic ranking does not search the corpus again. If the correct passage is absent from the hybrid top 50, the semantic stage cannot recover it. See 01_azure_ai_search_reranking.ipynb for the complete setup and query path. Test results for Azure AI Search These examples show that semantic reranking improves relevance selectively, not universally. It strongly helps the construction query, moving the correct passage from rank 4 to rank 1, but slightly degrades the Super Bowl and Tesla queries by one position. This reinforces that semantic ranking should be evaluated across a representative query set using aggregate metrics such as MRR or NDCG, rather than judged from a single result. Azure SQL Database: vector retrieval plus external Cohere reranking How it works. The Azure SQL notebook retrieves 20 candidates with exact cosine distance and sends their text to Cohere Rerank v4.0 Fast through sys.sp_invoke_external_rest_endpoint. The vector column and query vector must have the same dimensions. This repository uses 1,536-dimensional embeddings: SELECT TOP (@ candidate_count) context_id, title, content, 1 - VECTOR_DISTANCE( 'cosine', CAST(@ query_vector AS VECTOR(1536)), embedding ) AS similarity FROM dbo.documents ORDER BY similarity DESC; For reranking, we selected Cohere Rerank v4.0 Fast (Cohere-rerank-v4.0-fast), a fast version of Cohere’s fourth-generation relevance-ranking model. The model is deployed in Microsoft Foundry, where its Azure Direct inference endpoint is available in the deployment details within the Foundry portal. Azure SQL can call REST APIs directly using sp_invoke_external_rest_endpoint. Because Azure SQL allowlists Azure AI’s *.cognitiveservices.azure.com domain, we translate the equivalent Foundry endpoint from *.services.ai.azure.com while preserving the Cohere reranking route. from urllib.parse import urlsplit, urlunsplit def sql_compatible_endpoint(endpoint: str) -> str: """Convert an Azure Direct endpoint to Azure SQL's allowed hostname.""" parts = urlsplit(endpoint) if parts.hostname.endswith(".services.ai.azure.com"): resource = parts.hostname.removesuffix(".services.ai.azure.com") hostname = f"{resource}.cognitiveservices.azure.com" elif parts.hostname.endswith(".cognitiveservices.azure.com"): hostname = parts.hostname else: raise ValueError("Expected an Azure AI Services endpoint.") return urlunsplit( (parts.scheme, hostname, parts.path, parts.query, "") ) Then I defined a re-rank with cohere function, starting by loading the endpoint and setting the authentication: def rerank_with_cohere( cursor, question: str, candidates: list[dict], top_n: int = 10, ) -> list[dict]: """ Rerank candidate documents by calling Cohere through Azure SQL. Each candidate must contain a 'content' field. """ if not candidates: return [] sql_endpoint = sql_compatible_endpoint( os.environ["COHERE_RERANK_ENDPOINT"] ) model = os.environ["COHERE_RERANK_MODEL"] access_token = credential.get_token( "https://cognitiveservices.azure.com/.default" ).token headers = json.dumps({"Authorization": f"Bearer {access_token}"}) payload = json.dumps( { "model": model, "query": question, "documents": [row["content"] for row in candidates], "top_n": min(k, len(candidates)), }, ensure_ascii=False, ) cursor.execute( """ DECLARE @url NVARCHAR(4000) = CAST(? AS NVARCHAR(4000)); DECLARE @headers NVARCHAR(4000) = CAST(? AS NVARCHAR(4000)); DECLARE Payload NVARCHAR(MAX) = CAST(? AS NVARCHAR(MAX)); DECLARE Response NVARCHAR(MAX); DECLARE @status INT; EXEC @status = sys.sp_invoke_external_rest_endpoint @url = @url, @method = 'POST', @headers = @headers, Payload = Payload, @timeout = 60, @retry_count = 2, Response = Response OUTPUT; SELECT @status, Response; """, sql_endpoint, headers, payload, ) status, response_text = cursor.fetchone() if status != 0: raise RuntimeError(f"Reranker endpoint returned HTTP status {status}.") response = json.loads(response_text)["result"] You can see the complete implementation in the 02_azure_sql_reranking.ipynb notebook. Test results for Azure SQL Db Across the three sample questions, Cohere reranking consistently moved the correct SQuAD passage closer to the top: from rank 5 to 1 for the Super Bowl question, 3 to 2 for the Tesla question, and 8 to 1 for the construction question. These examples show how vector search provides a strong candidate set, while reranking applies deeper query-document relevance scoring to improve the final ordering. The results are illustrative rather than a complete quality benchmark, so broader evaluation across many queries is still recommended. Azure Database for PostgreSQL flexible server: pgvector, SQL RRF, and an optional model How it works: PostgreSQL makes the pipeline components explicit. The notebook uses pgvector for vector similarity, PostgreSQL full-text search for keyword retrieval, and SQL to implement RRF. Vector retrieval uses cosine distance: SELECT context_id, title, content, 1 - (embedding <= > % (query_vector) s:: vector) AS similarity FROM squad_docs ORDER BY embedding <= > % (query_vector) s:: vector LIMIT % (candidate_count) s; The hybrid query independently ranks vector and keyword hits, then combines positions rather than raw scores: SELECT d.context_id, COALESCE(1.0 / (60 + v.rank), 0) + COALESCE(1.0 / (60 + k.rank), 0) AS rrf_score FROM squad_docs AS d LEFT JOIN vector_hits AS v USING (context_id) LEFT JOIN keyword_hits AS k USING (context_id) WHERE v.context_id IS NOT NULL OR k.context_id IS NOT NULL ORDER BY rrf_score DESC; This is not a built-in PostgreSQL RRF operator. It is transparent, hand-written SQL over native retrieval primitives, which makes weighting and debugging flexible but leaves implementation and tuning with the application team. The notebook's optional learned stage sends the vector candidates from Python to a Foundry deployment of Cohere Rerank v4.0 Fast. This path was chosen because the tested Flexible Server azure_ai extension version expected the older serverless reranking endpoint contract. Microsoft documentation still describes azure_ai.rank() as a preview function whose default model is Cohere Rerank v3.5, even though that model retired on May 14, 2026. Treat this as a version-specific compatibility issue and verify current extension behavior before selecting an architecture. Azure HorizonDB is a different product path. Its AI Model Management feature can provision Cohere Rerank v4.0 Fast as default-reranker, but that management feature is currently a limited preview. It should not be described as a generally available Flexible Server capability. See 03_azure_postgres_reranking.ipynb for the full SQL and optional external model path. Test results for Azure SQL for PostgreSQL Flexible Server The tests show that PostgreSQL vector search provides a useful candidate set, SQL RRF can substantially improve results when keyword evidence is strong, and the Cohere semantic reranker is the most consistent overall: it moved the correct passage to rank 1 in two tests and from rank 3 to rank 2 in the Tesla test. RRF produced the biggest gain for the construction question, moving the correct passage from outside the vector top five to rank 1, but did not improve every query. The scores across stages are not directly comparable because cosine similarity, RRF score, and Cohere relevance use different scales. Azure Cosmos DB for NoSQL: hybrid search with built-in RRF How it works: Azure Cosmos DB for NoSQL supports native hybrid ranking with VectorDistance, FullTextScore, and RRF inside ORDER BY RANK: SELECT TOP K C.context_id, c.title, c.text FROM c ORDER BY RANK RRF( VectorDistance(c.vector, @query_vector), FullTextScore(c.text, @term1, @term2, @term3) ) The notebook extracts distinct terms from the question before building the full-text part of the query. That token selection is application logic and can materially affect the hybrid ranking, so production evaluation should test analyzers, languages, term extraction, and optional RRF weights. Cosmos DB Semantic Reranker is an SDK-integrated preview feature. The application first runs a query, serializes the resulting documents, and submits those documents with the user's context string: result = container.semantic_rerank( context=question, documents=documents, options={ "return_documents": False, "top_k": min(k, len(documents)), "sort": True, "document_type": "json", "target_paths": "title,text", }, ) The service accepts at most 50 documents per rerank call and returns relevance scores from 0 to 1, plus inference latency and token usage. It uses the Microsoft semantic ranking model also used by Azure AI Search. The reranking call requires Microsoft Entra authentication, the appropriate Semantic Reranker role, and an account-linked inference endpoint. The 04_azure_cosmosdb_reranking.ipynb in the shared repo contains and end-to-end implementation. Test results for Azure Cosmos Db The results show that vector search provides a strong baseline, while hybrid RRF and semantic reranking improve different queries in different ways. Hybrid RRF helps when exact keywords matter, moving the construction answer into the top results, while the semantic reranker delivers the strongest overall ordering, promoting the correct construction passage from hybrid rank 3 to rank 1 and improving the Super Bowl answer from rank 5 to rank 2. However, it does not always place the gold passage first, as seen in the Tesla example, confirming that reranking improves relevance but is query-dependent and should be evaluated across a larger test set. What the examples do and do not show The four services expose different ownership boundaries: • Azure AI Search owns hybrid fusion and learned semantic ranking inside the search service. • Azure SQL owns vector retrieval and outbound REST invocation in this example, while Foundry owns model inference. • PostgreSQL supplies vector and full-text primitives; the application owns the RRF SQL and optional Cohere call. • Cosmos DB provides native hybrid RRF and integrates a separate preview inference call through its SDK. Across three selected questions, the known source passage often moved substantially. That supports the practical value of testing a second-stage ranker. It does not prove that semantic reranking always improves top-1 accuracy, that RRF is universally beneficial, or that scores from different stages can be compared directly. Cosine similarity, RRF score, Azure AI Search reranker score, Cohere relevance, and Cosmos DB semantic relevance all have different definitions and scales. Compare rank positions and task-level metrics, not raw values across systems. Turn the demonstration into an evaluation For a production RAG system, convert the notebook pattern into a repeatable evaluation harness: Build a representative labeled query set from real user tasks. Freeze corpus, chunking, embedding model, dimensions, and candidate counts for each run. Record ranks after retrieval, fusion, and learned reranking. Measure Recall@k or Hit@k to verify that retrieval finds relevant evidence. Measure Mean Reciprocal Rank (MRR) when the position of the first relevant result matters. Use NDCG when judgments include multiple passages or graded relevance. Record latency percentiles, inference usage, request cost, and failure rates. Evaluate the generated answer separately for correctness, citation support, and refusal behavior. Also test the operational cases that a three-question demonstration cannot cover: empty keyword results, missing gold passages, long documents, multilingual text, filters, partial outages, token expiration, throttling, model retirement, and low-confidence scores. Practical guidance Retrieve broadly enough that the correct evidence can reach the learned stage. Use RRF when vector and keyword retrieval provide complementary signals. Rerank a bounded candidate set, commonly 20 to 50 passages, and measure the latency cost. Keep citations and source identifiers through every rank transformation. Version the corpus, embedding model, dimensions, query set, and reranker deployment. Do not hard-code assumptions about model endpoints or lifecycle dates. Verify current service documentation and the deployed extension or SDK version. Add thresholds or fallback behavior only after calibrating scores on your own data. Judge the full RAG chain. Better passage order is valuable only when it improves grounded answers for users. Vector search is built to find plausible candidates quickly. Rank fusion can reconcile retrieval signals, and a learned reranker can decide which candidates best address the question. The right architecture depends on where your data lives, which service boundaries you want to operate, and what your evaluation says about quality, latency, and cost. Resources Companion repository Azure AI Search semantic ranker Azure SQL VECTOR_DISTANCE Azure SQL sp_invoke_external_rest_endpoint Azure Database for PostgreSQL AI functions Microsoft Foundry model retirement schedule Azure Cosmos DB hybrid search Azure Cosmos DB Semantic Reranker SQuAD dataset card Dataset attribution Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP 2016. SQuAD 1.1 is distributed under CC BY-SA 4.0.Give Your E-Commerce App a Memory: Adding Agents That Actually Remember Your Customers
Ever shopped online and felt like the app had no idea who you are? You browse jackets every week, you told the chatbot you hate polyester, and yet it keeps showing you the same generic recommendations. That’s the problem. Most e-commerce apps treat every interaction as a blank slate. What if your app could remember? What if a customer could say “I told you last week I like leather jackets” and the app actually knew that? That’s what we’re building here — an AI shopping assistant with persistent memory, powered by Microsoft Agent Framework and SQL Server. The Problem: Amnesia in E-Commerce Traditional e-commerce chatbots have a fundamental issue — they forget everything the moment the session ends. Here’s what that looks like in practice: Monday: > Customer: “I’m looking for a warm winter jacket, something in leather” > Bot: “Great! Here are some leather jackets…” Wednesday: > Customer: “Show me more options like what we discussed” > Bot: “I’m sorry, could you tell me what you’re looking for?” The customer told you their preferences. They invested time in a conversation. And the app just… forgot. This isn’t just a bad user experience — it’s a missed opportunity. Every preference a customer shares is data you could use to serve them better next time. The Solution: An Agent That Remembers At a high level, what we want is simple: Chats naturally — the customer can talk about what they like and don’t like. Remembers across sessions — log out, come back tomorrow, and it still knows you prefer leather over polyester. Makes smart recommendations — uses the full conversation history to suggest products that actually match. The trick isn’t building a chatbot — that part is easy these days. The trick is giving it memory that persists and scales. Our Architecture The architecture has three layers: a FastAPI backend serving a browser SPA, conversational agents built on Microsoft Agent Framework, and SQL Server as the persistent memory layer. Architecture diagram The key piece that ties it all together is the history provider — a component that plugs into the framework and handles loading/saving conversation history automatically. The agent doesn’t manage its own memory; the framework does, through this provider abstraction. Why Microsoft Agent Framework Microsoft Agent Framework is an open-source Python framework for building AI agents. Think of it as the plumbing between your application logic and the LLM — it handles sessions, conversation history, context injection, and tool execution so you can focus on what your agent actually does. Why use it instead of rolling your own? Session management — built-in support for creating and tracking user sessions. Context providers — a clean abstraction for injecting history, user profiles, or any other context before each LLM call. Provider pattern — swap out your storage backend (SQL Server, Cosmos DB, in-memory) without changing agent code. Tool integration — define functions the agent can call, and the framework handles the execution loop. At its simplest, creating an agent looks like this: from agent_framework import Agent agent = Agent( client=chat_client, instructions="You are a helpful assistant.", ) session = agent.create_session() response = await agent.run("Hello!", session=session) print(response) That gives you a stateless agent — no memory between calls. To add memory, you provide a context provider that loads and saves messages: from agent_framework import Agent, BaseHistoryProvider class MyHistoryProvider(BaseHistoryProvider): async def get_messages(self, session_id, **kwargs): # Load messages from your storage return load_from_db(session_id) async def save_messages(self, session_id, messages, **kwargs): # Persist messages to your storage save_to_db(session_id, messages) agent = Agent( client=chat_client, instructions="You are a helpful assistant.", context_providers=[MyHistoryProvider()] ) The framework calls get_messages() before each run and save_messages() after. Your agent now has memory — and you didn’t have to manually wire load/save into every request handler. Why SQL Server for the Memory Layer So, we need a database behind that history provider. Why SQL Server over, say, PostgreSQL? Both are solid, relational databases. Both can store conversation history just fine. But for this use case — agent memory that starts local and grows to production — SQL Server has a smoother story: Consideration SQL Server PostgreSQL Local dev One Docker command, no config files Needs pg_hba.conf, postgresql.conf tuning Cloud path Docker → Azure SQL Database, same driver, zero code changes Docker → various managed options (Cloud SQL, RDS, Azure DB for PostgreSQL), often with driver/extension differences Managed scaling Azure SQL auto-scales compute, Hyperscale handles 100TB+, license-free option Managed Postgres varies by provider, Citus for scale-out adds complexity Free tier 10 free databases per Azure subscription Varies by cloud provider Agent framework fit First-class mssql_python driver, tested with MAF samples Works, but you’re wiring your own driver integration The short version: PostgreSQL is a great database, but SQL Server gives us a single continuum from docker run on a laptop all the way to a globally distributed managed service — same engine, same queries, same connection driver. When your agent goes from prototype to production, you change a connection string, not your architecture. We’ll go deeper on the cloud scaling story later in this post. For now, let’s build the thing. Setting Up the Infrastructure Getting SQL Server running locally is one Docker command: docker run -d ` --name sql ` -e "ACCEPT_EULA=Y" ` -e "MSSQL_SA_PASSWORD=YourStrong!Passw0rd" ` -p 1433:1433 ` -v sqlvolume:/var/opt/mssql ` mcr.microsoft.com/mssql/server:2022-latest We also need local LLMs via Ollama — Llama 3.1 for conversational quality and Phi-3 Mini for fast structured recommendations: foundry download llama3.1 foundry download phi3:mini And then our Python dependencies: cd commerce-agent uv sync uv pip install fastapi uvicorn httpx The Database Schema The schema is straightforward — Users, Sessions, and ChatHistory. The important relationship is that ChatHistory is scoped to a session, and sessions belong to users. This means each user gets their own isolated conversation history. CREATE TABLE Users ( Id INT IDENTITY PRIMARY KEY, Username NVARCHAR(100) UNIQUE NOT NULL, DisplayName NVARCHAR(200) NOT NULL, CreatedAt DATETIME2 DEFAULT GETUTCDATE() ) CREATE TABLE Sessions ( Id NVARCHAR(100) PRIMARY KEY, UserId INT NOT NULL FOREIGN KEY REFERENCES Users(Id), CreatedAt DATETIME2 DEFAULT GETUTCDATE(), LastActiveAt DATETIME2 DEFAULT GETUTCDATE() ) CREATE TABLE ChatHistory ( Id INT IDENTITY PRIMARY KEY, SessionId NVARCHAR(100) NOT NULL FOREIGN KEY REFERENCES Sessions(Id), Role NVARCHAR(50), Content NVARCHAR(MAX), CreatedAt DATETIME2 DEFAULT GETUTCDATE() ) Every message — whether from the user or the assistant — gets stored with a timestamp and role. When the agent needs context, it pulls the full conversation history for that session. The History Provider: Plugging Memory into the Framework Here’s where it gets interesting. Microsoft Agent Framework has a concept called BaseHistoryProvider. You extend it, implement two methods — get_messages() and save_messages() — and the framework handles the rest. It calls get_messages() before each agent run to load context, and save_messages() after to persist new messages. from agent_framework import BaseHistoryProvider, Message class CommerceHistoryProvider(BaseHistoryProvider): def __init__(self, source_id: str = "commerce-history"): super().__init__(source_id) async def get_messages( self, session_id: str | None, *, state: dict[str, Any] | None = None, **kwargs: Any ) -> list[Message]: if not session_id: return [] conn = get_conn() cursor = conn.cursor() cursor.execute(""" SELECT Role, Content FROM ChatHistory WHERE SessionId = ? ORDER BY CreatedAt """, (session_id,)) rows = cursor.fetchall() conn.close() return [Message(role=role, text=content) for role, content in rows] async def save_messages( self, session_id: str | None, messages: Sequence[Message], *, state: dict[str, Any] | None = None, **kwargs: Any, ) -> None: if not session_id: return conn = get_conn() cursor = conn.cursor() for msg in messages: text = msg.text or "" if not text and msg.contents: text = "".join(c.text for c in msg.contents if hasattr(c, "text")) cursor.execute( "INSERT INTO ChatHistory (SessionId, Role, Content) VALUES (?, ?, ?)", (session_id, msg.role, text) ) conn.commit() conn.close() That’s it — that’s the memory layer. The framework calls these methods at the right time, so you never have to manually load or save history in your route handlers. Wiring It Up: The Agent With the history provider in place, creating the agent is clean: from agent_framework import Agent history_provider = CommerceHistoryProvider() chat_client = create_chat_client() agent = Agent( client=chat_client, instructions=( "You are a friendly shopping assistant. Help users discover products they'll love. " "Ask about their interests, hobbies, and preferences. Remember what they tell you. " "Be conversational and warm." ), context_providers=[history_provider] ) The context_providers parameter is the key. By passing our history provider here, the agent automatically gets the user’s full conversation history as context before generating a response. No manual plumbing required. Handling a Chat Request When a user sends a message, here’s what happens end-to-end: app.post("/api/chat") async def chat(req: ChatRequest): user = get_user(req.username) if not user: raise HTTPException(status_code=401, detail="Not logged in") session_id = get_or_create_session(user["id"]) session = agent.create_session(session_id=session_id) response = await agent.run(req.message, session=session) return {"response": str(response)} Behind the scenes: 1. We look up (or create) a session for this user. 2. The framework calls get_messages() to load all prior conversation. 3. The LLM sees the full history + the new message and generates a contextual response. 4. The framework calls save_messages() to persist the new exchange. The customer says “I told you I like leather jackets” and the agent actually knows because it has the full history. Smart Recommendations The real payoff comes when you combine memory with recommendations. Because we have the full conversation history, we can analyze what the customer has told us and match against our product catalog: app.post("/api/recommendations") async def recommendations(req: RecommendationRequest): user = get_user(req.username) session_id = get_or_create_session(user["id"]) history = get_session_history(session_id) if not history: all_prods = get_all_products()[:6] return {"best_match": all_prods[0], "other": all_prods[1:]} matched = score_products(history) return { "best_match": matched[0] if matched else None, "other": matched[1:] if len(matched) > 1 else matched, "message": f"Based on your preferences, {user['display_name']}!" } The score_products() function takes the conversation history, extracts preferences, and scores products against them. If a customer said they love outdoor gear and hate synthetic materials — that’s reflected in what gets recommended. Why This Matters Adding persistent memory to your e-commerce agent isn’t just a technical exercise. It fundamentally changes the customer relationship: Customers feel heard — they don’t have to repeat themselves. Recommendations improve over time — the more they chat, the better you understand them. Sessions become cumulative — each visit builds on the last instead of starting fresh. The Microsoft Agent Framework makes this surprisingly straightforward. You implement a history provider, plug it in via context_providers, and the framework handles the lifecycle. SQL Server gives you durable, queryable storage. And because the provider interface is clean, moving to the cloud doesn’t require rewriting anything. Growing Up: From Docker to the Cloud We wanted to start easy — Foundry Local for the LLM, SQL Server from a Docker container, everything running on your laptop. That’s great for prototyping and proving out the concept. But what does the grow-up story look like when you’re ready to serve real customers at scale? Let’s talk about that next. The good news: because we used SQL Server locally, the path to production is a straight line — not a migration. Azure SQL Database Azure SQL Database is the managed version of what you’ve been running in Docker. Same engine, same T-SQL, same connection driver. Your CommerceHistoryProvider code doesn’t change at all — you just update the connection string. What you get by moving to Azure SQL Database: Feature Why it matters for agents Auto-scaling Conversation spikes during sales events? The database scales compute up and back down automatically. 10 free databases per subscription Experiment with separate DBs per agent or environment without worrying about cost during development. Built-in high availability 99.99% SLA — your agent’s memory doesn’t go down because a container crashed. Geo-replication Serve users globally with read replicas close to them — conversation history loads fast regardless of region. Automatic backups Point-in-time restore up to 35 days. Accidentally dropped the ChatHistory table? Roll back. Hyperscale: When Conversations Get Big As your user base grows, conversation history grows with it. A single user might accumulate thousands of messages over months. Multiply that by millions of users and you’re looking at serious storage. Azure SQL Hyperscale is designed for exactly this: Up to 100 TB of storage — your conversation history can grow without partition gymnastics. License-free — Hyperscale has a license-free option, so you only pay for compute and storage, not per-core licensing. Near-instant scale-out — add read replicas in seconds for analytics workloads (e.g., “what are the trending preferences across all users this week?”). Fast database snapshots — spin up a copy of production for testing or ML training without waiting hours for a restore. The Connection String Is the Only Change Here’s what the transition looks like in code. Your local setup: DB_CONFIG = { "server": "localhost", "port": 1433, "user": "sa", "password": "YourStrong!Passw0rd", "database": "agentdb" } Your production setup on Azure SQL: DB_CONFIG = { "server": "your-agent-db.database.windows.net", "port": 1433, "user": "agent-app", "password": os.environ["AZURE_SQL_PASSWORD"], "database": "agentdb" } Same schema. Same queries. Same CommerceHistoryProvider. The agent doesn’t know or care that it moved from a Docker container to a globally distributed managed database — it just works, faster and more reliably. See It in Action Here’s Steve chatting with the assistant about outdoor gear, with Foundry selected as the recommendation provider. Notice how the recommendations on the right reflect his stated preferences: Steve chatting with the shopping agent — Foundry provider selected And here’s Marla, a completely different user with different tastes. Same app, same agent — but her conversation history and recommendations are entirely her own: Marla chatting with the shopping agent — Foundry provider selected Each user gets isolated conversation history. The agent remembers what they said, not what someone else said. That’s the power of session-scoped memory backed by SQL Server. Running It Yourself 1. Clone the repo: https://github.com/softchris/ecommerce-agent-memory 2. Install dependencies (make sure you installed the prereqs as laid out by the README file first) uv sync 3. Run the app uv run uvicorn app:app --reload --port 8000 4. Navigate to http://localhost:8000, log in as Marla or Steve, and start chatting. Tell the assistant what you like. Log out. Come back. Ask for recommendations. The agent remembers. That’s the difference between a chatbot and an assistant that actually knows your customers. Call to Actions Ready to build your own agent with memory? Here’s where to go next: 📖 Microsoft Agent Framework Documentation — official docs covering agents, context providers, sessions, tool use, and more. Start here to understand the full capabilities of the framework. 🧪 Foundry Local Python Samples — hands-on sample code showing how to run agents locally with Foundry. Great for getting something running fast without cloud dependencies. 🛍️ This project’s source code — the full e-commerce agent with persistent SQL Server memory. Clone it, run it, and adapt it to your own use case.MCP Server Authorization with Azure API Management: From Simple to Advanced
Why put API Management in front of your MCP servers The Model Context Protocol (MCP) has quickly become the standard way for AI agents, such as GitHub Copilot in VS Code, to reach external tools and data. As soon as an MCP server does anything meaningful, the same questions that govern any API resurface: who is allowed to call it, what are they allowed to do, and how do you enforce that consistently across many servers without rewriting each one. Azure API Management (APIM) answers those questions for MCP. It sits between the MCP client and the tool backend and applies the controls you already trust for REST APIs: identity validation, OAuth, rate limiting, IP filtering, and observability. Crucially, APIM speaks the MCP authorization specification, which is built on OAuth 2.1 and Protected Resource Metadata (PRM, RFC 9728). That means APIM can do more than block bad requests. It can actively drive an interactive sign-in from the IDE, so the user logs in with their own identity and the agent acts on their behalf. This article walks through a progression of authorization scenarios, each one building on the last: The simple case: validate a token and block everything else. Triggering an interactive sign-in from VS Code for an MCP server that APIM hosts from your own APIs. Going beyond "is this a tenant user" to "does this user have the right attribute" with Entra app roles. Fronting an existing external MCP server and letting it drive its own OAuth flow (GitHub as the example). Governing which tools of an existing MCP server an agent is actually allowed to invoke. APIM MCP capabilities and the basic authorization options API Management exposes MCP servers in two distinct ways, and the authorization story differs slightly for each. Expose a REST API as an MCP server. APIM takes an API it already manages and projects selected operations as MCP tools. You own the operations, so you choose exactly which ones become tools at configuration time. This is the right mode when the capability you want to expose is an API you control. Expose an existing MCP server (passthrough). APIM fronts a remote MCP-compatible server (LangChain, an Azure Function, GitHub's remote MCP server, your own container) and relays the MCP protocol to it. APIM governs access, but the upstream server still owns its tool catalog. On top of either mode, you have a spectrum of authorization options: Subscription keys for simple, machine-to-machine access where a shared secret in a header is acceptable. Token validation with Microsoft Entra ID, where APIM acts as the protected resource and verifies a bearer token on every call. Interactive OAuth 2.1 sign-in, where APIM advertises Protected Resource Metadata so an MCP client can discover the authorization server, log the user in, and retry with a user token. Authorization passthrough, where an external MCP server presents its own authorization challenge and APIM relays it faithfully so the client authenticates directly against the upstream's identity provider. The rest of the article works through these options in increasing order of capability. The example setup The walkthroughs in the first three scenarios all use the same backend so you can reproduce them without standing up anything of your own: the publicly available Star Wars API at Star Wars API. It is a simple, read-friendly REST API (characters, films, planets, starships, and so on) imported into API Management as a normal API and then projected as an MCP server. The reason this single API is enough to illustrate the whole progression is that, in API Management, one underlying API can back several independent MCP servers, each exposing a different slice of its operations. For example, you can create: A read-only MCP server that exposes only the GET operations, for agents that should be able to query data but never change it. A write-capable MCP server that exposes the POST, PUT, or DELETE operations, for trusted automation that is allowed to mutate state. Same backend API, two MCP servers, two different tool surfaces. Each of these servers is an independent resource in APIM, so each one can carry its own authorization. Both can require an authenticated user (Scenarios 1 and 2), and you can go further by protecting only the sensitive one: gate the write-capable server behind an Entra app role so that, even among authenticated users, only those who carry a specific claim can reach the mutating tools. That app-role mechanism is the subject of Scenario 3, and it composes naturally with the multi-server split described here. Registering the MCP API in Microsoft Entra ID Before any of the policies below can validate a token, you need an application registration in Microsoft Entra ID that represents the MCP API. This registration is what defines the audience and scope that tokens are issued for, and it is the source of the mcp-audience, mcp-scope, and (indirectly) mcp-client-id values that the policies reference. Create it once and reuse it across all the MCP servers in this article. In the Azure portal, open Microsoft Entra ID, then App registrations, then New registration. Name it (for example, star-wars-mcp-api), choose single-tenant, and register. Record the Application (client) ID and the Directory (tenant) ID. Open Expose an API and add an Application ID URI. Accept the default api://<app-id>. This URI is your token audience. Still under Expose an API, add a delegated scope named MCP.Access, set its consent display name and description, set the state to Enabled, and save. Authorize the client that will request the scope. Under Expose an API, select Add a client application and enter the client ID of the MCP client. For VS Code, this is the built-in Microsoft authentication client aebc6443-996d-45c2-90f0-388ff96faa56. Check the MCP.Access scope and save. These steps produce the four constants the validation policy needs: Named value Comes from Example entra-tenant-id The Directory (tenant) ID from step 1 11111111-1111-1111-1111-111111111111 mcp-audience The Application ID URI from step 2 api://22222222-2222-2222-2222-222222222222 mcp-scope The scope name from step 3 MCP.Access mcp-client-id The client ID of the calling app from step 4 aebc6443-996d-45c2-90f0-388ff96faa56 [!NOTE] mcp-client-id is the identity of the application calling the MCP server, not the MCP API itself. For VS Code it is the built-in Microsoft authentication client, and its value lands in the token's appid claim, which is why the validation policy lists it under client-application-ids. If your tenant blocks the first-party VS Code client, register your own public client application and use its client ID instead. [!TIP] For the privileged-access feature in Scenario 3, you will also declare an app role on this same registration. You do not need it yet, but it is convenient to know that all identity configuration for these servers lives on this one app registration. With that backend and structure in mind, the scenarios below build up the authorization model one capability at a time. Scenario 1: The simple case, validate the token and block unauthorized access The most basic protection is to require a valid Entra ID token on every MCP request and reject anything that fails validation. No interactive flow, no roles, just a gate. APIM does this with the validate-azure-ad-token policy. The policy checks the issuing tenant, the audience (your MCP API), the calling client application, and the required scope. Anything that does not satisfy all four is rejected with a 401. <policies> <inbound> <base /> <validate-azure-ad-token tenant-id="{{entra-tenant-id}}" header-name="Authorization" failed-validation-httpcode="401" failed-validation-error-message="Unauthorized. Access token is missing or invalid."> <client-application-ids> <application-id>{{mcp-client-id}}</application-id> </client-application-ids> <audiences> <audience>{{mcp-audience}}</audience> </audiences> <required-claims> <claim name="scp" match="any"> <value>{{mcp-scope}}</value> </claim> </required-claims> </validate-azure-ad-token> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> </on-error> </policies> The values in double braces are APIM named values: centralized constants, defined once and shared by every MCP server. They map directly to the four values produced by the Entra app registration in the example setup (entra-tenant-id, mcp-audience, mcp-scope, and mcp-client-id). Storing them as named values keeps the policy free of hardcoded identifiers and lets every server reuse the same configuration. This gets you a server that nobody can call without a properly minted token. What it does not do is help a fresh client obtain that token in the first place. That is the next scenario. Scenario 2: Driving an interactive sign-in from VS Code for an APIM-hosted MCP server When you expose one of your own APIs as an MCP server, you usually want a developer to open VS Code, connect to the server, and be prompted to sign in with their Microsoft account. No pre-shared key, no manual token handling. APIM achieves this by behaving as a well-mannered OAuth 2.1 protected resource. Using the Star Wars MCP server from the example setup, each selected operation becomes a tool the agent can call, so an agent can answer "which films featured the character named Leia" by calling the underlying API through APIM. How the sign-in flow works The protocol choreography is what turns a plain 401 into an interactive login: Two ingredients make this work: a 401 challenge that points to a metadata document, and the metadata document itself. The challenge: a 401 that points the client to its metadata Instead of a bare 401, APIM returns a WWW-Authenticate header carrying the URL of the server's Protected Resource Metadata. This is what tells the client "you need a token, and here is where to learn how to get one." Keeping this logic in a shared policy fragment means every MCP server reuses it. Notice the mcpResourceMetadataUrl reference in the fragment below. It is not hardcoded; it is a context variable that each MCP server sets in its own server-level policy before including this fragment (you will see that wiring in the per-server policy later in this scenario). The fragment simply reads whatever value the calling server provided. This indirection is what keeps the fragment pluggable: the same shared challenge-and-validate logic serves every MCP server, while each server supplies its own PRM URL. In most deployments the PRM endpoint is a single, dynamic one (built in the next section) that derives the resource from the request path, so the variable just carries that server's path. But because the URL is configurable per server rather than baked into the fragment, you retain flexibility for the cases that need it. <fragment> <!-- No token: challenge with the per-server PRM URL set by the caller --> <choose> <when condition="@(!context.Request.Headers.ContainsKey("Authorization"))"> <return-response> <set-status code="401" reason="Unauthorized" /> <set-header name="WWW-Authenticate" exists-action="override"> <value>@("Bearer resource_metadata=\"" + (string)context.Variables.GetValueOrDefault("mcpResourceMetadataUrl", "") + "\"")</value> </set-header> </return-response> </when> </choose> <!-- Token present: validate against shared named values --> <validate-azure-ad-token tenant-id="{{entra-tenant-id}}" header-name="Authorization" failed-validation-httpcode="401" failed-validation-error-message="Unauthorized. Access token is missing or invalid."> <client-application-ids> <application-id>{{mcp-client-id}}</application-id> </client-application-ids> <audiences> <audience>{{mcp-audience}}</audience> </audiences> <required-claims> <claim name="scp" match="any"> <value>{{mcp-scope}}</value> </claim> </required-claims> </validate-azure-ad-token> </fragment> Creating the /.well-known PRM endpoint in APIM with a policy This is the part that often surprises people: APIM itself serves the metadata document. There is no separate identity service to stand up. You publish one small anonymous API at the service root that answers GET /.well-known/oauth-protected-resource/*, derives the resource value from the requested path, and returns a JSON document pointing at Microsoft Entra ID as the authorization server. Create a blank HTTP API named well-known with an empty API URL suffix so it resolves at the service root, add a GET operation with the template /.well-known/oauth-protected-resource/*, clear the subscription requirement so it is reachable anonymously, and apply this policy: <policies> <inbound> <base /> <!-- Build the resource URL from the requested PRM sub-path --> <set-variable name="resourceUrl" value="@{ var prefix = "/.well-known/oauth-protected-resource"; var path = context.Request.OriginalUrl.Path; var resourcePath = path.Length > prefix.Length ? path.Substring(prefix.Length) : ""; return "https://" + context.Request.OriginalUrl.Host + resourcePath; }" /> <return-response> <set-status code="200" reason="OK" /> <set-header name="Content-Type" exists-action="override"> <value>application/json</value> </set-header> <set-body>@{ return new JObject( new JProperty("resource", (string)context.Variables["resourceUrl"]), new JProperty("authorization_servers", new JArray( "https://login.microsoftonline.com/{{entra-tenant-id}}/v2.0")), new JProperty("scopes_supported", new JArray("{{mcp-prm-scope}}")), new JProperty("bearer_methods_supported", new JArray("header")) ).ToString(); }</set-body> </return-response> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> </on-error> </policies> The {{mcp-prm-scope}} named value populates the scopes_supported array of the metadata document. It tells the client which delegated scope to request when it goes to the authorization server, so it must be the fully qualified scope value: the token audience (the Application ID URI from the app registration) followed by the scope name. With the example values that is api://22222222-2222-2222-2222-222222222222/MCP.Access. In other words, it is the combination of the mcp-audience and mcp-scope values defined in the example setup. Named value Value to set Example mcp-prm-scope <mcp-audience>/<mcp-scope> api://22222222-2222-2222-2222-222222222222/MCP.Access [!NOTE] Keep mcp-prm-scope in sync with the scope the validation fragment requires. The PRM document advertises this scope so the client requests it, and validate-azure-ad-token then checks for it in the scp claim. A mismatch means the client obtains a token without the scope APIM expects, and validation fails. Because the policy builds the resource value from the request path, this single endpoint serves metadata for every MCP server you ever add. The Star Wars server, a future inventory server, and anything else all share it. Wiring it onto the MCP server Each MCP server only needs to declare its own metadata URL and include the shared fragment: <policies> <inbound> <base /> <set-variable name="mcpResourceMetadataUrl" value="https://apim-contoso-mcp.azure-api.net/.well-known/oauth-protected-resource/star-wars-mcp/mcp" /> <include-fragment fragment-id="mcp-entra-auth" /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> <include-fragment fragment-id="mcp-auth-challenge-onerror" /> </on-error> </policies> On the VS Code side, the configuration is deliberately plain. With no subscription-key header present, the client falls straight into the OAuth flow: { "servers": { "star-wars-mcp": { "url": "https://apim-contoso-mcp.azure-api.net/star-wars-mcp/mcp", "type": "http" } } } Restart the server in VS Code, and it detects the 401, reads the metadata, opens a browser sign-in, requests consent on first use, and then loads the tools using the user's token. [!CAUTION] Do not read the response body with context.Response.Body inside MCP server policies. It forces response buffering and breaks the MCP streaming transport. If global diagnostic logging is enabled, set the Frontend Response payload bytes to log to 0 at the All APIs scope. Scenario 3: Beyond tenant membership, authorize on a user attribute with app roles Validating a token confirms the caller is a signed-in user in your tenant with the right scope. That is often not enough. Some MCP servers expose sensitive tools that only a subset of users should reach. You want to express "this user is not only part of the tenant, but has a specific attribute that permits this server." Microsoft Entra app roles are the optimal mechanism for this. You declare a role on the MCP API app registration, assign it to specific users or to a security group, and Entra ID emits a roles claim in the access token whenever your API is the audience. APIM then authorizes on that claim. App roles beat the groups claim here because they avoid the group overage problem, they are scoped to the application, and they travel with the app. Declaring and assigning the role On the MCP API app registration, under App roles, create a role: Setting Value Display name Privileged Access Allowed member types Users/Groups Value Privileged.Access Description Access to privileged MCP servers Then, on the matching enterprise application, under Users and groups, assign the users (or, better, a security group) to the Privileged Access role. The Value field is the exact string that lands in the token roles claim, so it cannot contain spaces. [!TIP] Keep User assignment required set to No on the enterprise application. Unassigned users still obtain a valid token with the MCP.Access scope and keep access to the non-privileged servers. They simply do not carry the roles claim, so the privileged servers reject them. Enforcing the claim in the per-server policy The shared mcp-entra-auth fragment is used by every server, so the role requirement must not live there. Place the check in the privileged server's own policy, right after the fragment include. The token is already validated at that point, so this step is pure authorization. Because the caller is authenticated but not authorized, return 403, not 401, and do not emit a challenge: re-authenticating will not grant a role the user does not have. <policies> <inbound> <base /> <set-variable name="mcpResourceMetadataUrl" value="https://apim-contoso-mcp.azure-api.net/.well-known/oauth-protected-resource/star-wars-mcp/mcp" /> <include-fragment fragment-id="mcp-entra-auth" /> <!-- Privileged guardrail: require the Privileged.Access app role --> <choose> <when condition="@(!context.Request.Headers.GetValueOrDefault("Authorization","").Replace("Bearer ","").AsJwt().Claims.GetValueOrDefault("roles", new string[0]).Contains("Privileged.Access"))"> <return-response> <set-status code="403" reason="Forbidden" /> <set-header name="Content-Type" exists-action="override"> <value>application/json</value> </set-header> <set-body>{"error":"forbidden","message":"You lack the Privileged.Access role required for this MCP server."}</set-body> </return-response> </when> </choose> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> <include-fragment fragment-id="mcp-auth-challenge-onerror" /> </on-error> </policies> One operational detail worth calling out: app-role assignments only appear in newly issued tokens. A user who is granted the role after they signed in must obtain a fresh token. In VS Code, run MCP: Reset Cached Tokens (or sign out of the Microsoft account from the Accounts menu), then restart the server and sign in again. You can confirm the result by pasting the access token into https://jwt.ms and checking for "roles": ["Privileged.Access"]. Scenario 4: Fronting an existing external MCP server that drives its own sign-in So far APIM has been the authorization resource. But many valuable MCP servers already exist and run their own identity. GitHub publishes a remote MCP server with dozens of tools, and it authenticates users against GitHub's own OAuth authorization server. You do not want to re-implement that. You want APIM to govern access (rate limits, IP rules, logging, a single managed endpoint) while letting the upstream own the login. This is the "expose an existing MCP server" passthrough mode. When you register GitHub's remote MCP server behind APIM, the gateway relays the upstream's own authorization challenge. The client never authenticates against Entra here. It authenticates directly against GitHub. The flow, confirmed by probing the gateway: A call to the APIM endpoint with no token returns GitHub's own 401 with a WWW-Authenticate header, relayed through APIM. The Protected Resource Metadata that GitHub serves advertises authorization_servers: ["https://github.com/login/oauth"], so the client knows to log in at GitHub. The PRM resource reflects the APIM host, because GitHub builds it from the forwarded Host header. The client trusts the APIM endpoint while still logging in at GitHub. VS Code completes the GitHub sign-in and the full tool catalog loads. In the proof of concept this surfaced all 47 GitHub tools through the single APIM endpoint. The client configuration is again just a URL pointing at APIM: { "servers": { "github-via-apim": { "url": "https://apim-contoso-mcp.azure-api.net/github-mcp/mcp", "type": "http" } } } The key insight is that APIM transparently relays the backend's authentication challenge. GitHub remains the authorization server, GitHub tolerates being fronted by APIM, and you get a governed, centrally managed entry point without owning the identity flow. [!NOTE] Passthrough only relays what the upstream advertises. If the backend's PRM resource value and the actual MCP transport endpoint differ by a path segment, some clients fall back to deriving the metadata location from the server URL and can miss it. When you onboard a custom self-authenticating server, verify that the resource it advertises matches the exact URL the client connects to. Scenario 5: Restricting which tools of an existing MCP server an agent may call Passthrough raises a governance question that token validation alone cannot answer. A developer may legitimately have permission to merge a pull request through GitHub, but you may not want their AI agent to perform that action autonomously. You want to allow the read and discovery tools while blocking the destructive write tools, at the gateway, regardless of what the client tries. What is and is not possible for an external server It is important to be precise here, because the capability differs from the REST-as-MCP mode: For a REST-API-exposed-as-MCP server, you pick which operations become tools at creation time. That is native tool selection and the cleanest possible filter. For an existing/external MCP server, APIM does not enumerate the upstream's tools. The portal Tools blade explicitly states that tools are not visible for external MCP servers, and there is no allow-list property for them. APIM also cannot safely rewrite the tools/list response, because reading the response body breaks the streaming transport and the list may arrive as text/event-stream. What APIM can do reliably, and server-agnostically, is block the invocation. Every tool call arrives as a JSON-RPC tools/call request in the request body, which APIM can inspect safely. The deny-listed tools remain visible in the catalog, but any attempt to invoke one is intercepted at the gateway and returned a JSON-RPC error before it ever reaches the upstream. The reusable deny-list fragment The block is driven by a per-server named value (a comma-separated list of tool names), so the same fragment governs every external server. Only the named value changes. <!-- Fragment: mcp-tool-filter (include after the auth fragment) --> <fragment> <choose> <when condition="@(context.Request.Body != null)"> <set-variable name="mcpMethod" value="@{ try { var body = context.Request.Body.As<JObject>(preserveContent: true); return (string)body?["method"] ?? string.Empty; } catch { return string.Empty; } }" /> <choose> <when condition="@(((string)context.Variables["mcpMethod"]).Equals("tools/call", StringComparison.OrdinalIgnoreCase))"> <set-variable name="mcpToolName" value="@{ var body = context.Request.Body.As<JObject>(preserveContent: true); return (string)body?["params"]?["name"] ?? string.Empty; }" /> <!-- mcpBlockedTools is a comma-separated deny-list set by the per-server policy before this include --> <set-variable name="mcpBlocked" value="@{ var tool = ((string)context.Variables["mcpToolName"]).Trim().ToLowerInvariant(); var deny = ((string)context.Variables.GetValueOrDefault("mcpBlockedTools", "")).ToLowerInvariant().Split(',').Select(t => t.Trim()); return deny.Contains(tool); }" /> <choose> <when condition="@((bool)context.Variables["mcpBlocked"])"> <return-response> <set-status code="200" reason="OK" /> <set-header name="Content-Type" exists-action="override"> <value>application/json</value> </set-header> <set-body>@{ var id = "null"; try { var body = context.Request.Body.As<JObject>(preserveContent: true); id = body?["id"]?.ToString(Newtonsoft.Json.Formatting.None) ?? "null"; } catch {} return "{\"jsonrpc\":\"2.0\",\"id\":" + id + ",\"error\":{\"code\":-32602,\"message\":\"Unknown tool: " + ((string)context.Variables["mcpToolName"]) + "\"}}"; }</set-body> </return-response> </when> </choose> </when> </choose> </when> </choose> </fragment> The deny-list itself lives in a named value, one per server: APIM named value. Comma-separated, case-insensitive. mcp-blocked-tools-github = merge_pull_request,create_repository,delete_repository,push_files,create_or_update_file,issue_write,label_write # <policies> <inbound> <base /> <set-variable name="mcpResourceMetadataUrl" value="https://apim-contoso-mcp.azure-api.net/.well-known/oauth-protected-resource/github-mcp/mcp" /> <include-fragment fragment-id="mcp-entra-auth" /> <set-variable name="mcpBlockedTools" value="{{mcp-blocked-tools-github}}" /> <include-fragment fragment-id="mcp-tool-filter" /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> <include-fragment fragment-id="mcp-auth-challenge-onerror" /> </on-error> </policies> Generic per-server pattern: mcp-blocked-tools-<server> = <comma,separated,tool,names> Wiring it onto the GitHub passthrough server <policies> <inbound> <base /> <set-variable name="mcpResourceMetadataUrl" value="https://apim-contoso-mcp.azure-api.net/.well-known/oauth-protected-resource/github-mcp/mcp" /> <include-fragment fragment-id="mcp-entra-auth" /> <set-variable name="mcpBlockedTools" value="{{mcp-blocked-tools-github}}" /> <include-fragment fragment-id="mcp-tool-filter" /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> <include-fragment fragment-id="mcp-auth-challenge-onerror" /> </on-error> </policies> Now when the agent tries to merge a pull request, the gateway returns a clean -32602 Unknown tool error and the upstream is never touched. Read and discovery tools continue to work. The tool still appears in the client's catalog. Adding governance for another external server is just one more named value plus the same fragment include. No new policy logic. Key takeaways API Management turns MCP servers into governed resources, applying the same identity, traffic, and observability controls you already use for APIs. Start simple with validate-azure-ad-token to gate access, then graduate to a full interactive sign-in by serving Protected Resource Metadata from a single APIM policy. You can publish multiple MCP servers from one underlying API, for example a read-only server and a read-write server, by selecting different operations. App roles let you authorize on a user attribute, not just tenant membership, and the check belongs in the per-server policy so shared logic stays clean. For existing external servers, APIM relays the upstream's own OAuth flow, so a server like GitHub keeps owning its identity while you keep central governance. When an external server's full tool surface is too broad, APIM can block specific tool invocations at the gateway with a reusable, named-value-driven policy, so a user's agent cannot perform actions the user could perform manually. References About MCP servers in Azure API Management Secure access to MCP servers in API Management Expose REST API in API Management as an MCP server Expose and govern an existing MCP server validate-azure-ad-token policy reference Policy fragments in API Management RFC 9728: OAuth 2.0 Protected Resource Metadata MCP authorization specification Star Wars API (example backend) MCP for BeginnersCreating Autonomous Teams Agents Using OpenClaw, MCP, and Azure Container Apps
The one shift that changes everything For two years, "AI coding" meant autocomplete. A suggestion appears in your editor, you hit tab, you move on. The agent only existed while you were actively typing. That is no longer the only model. A new category of tools runs asynchronously and autonomously: you message the agent from a chat window — Teams, Slack, Telegram — describe what you want, and walk away. The agent plans, writes code, runs tests, deploys, and hands you back a result. Some of them never sleep: they hold a persistent memory, load their own skills, and act on a schedule without being prompted. This is the world of OpenClaw, Hermes Agent, and the other long-running autonomous agents that exploded across developer culture in 2026. OpenClaw alone crossed 377,000 GitHub stars and millions of active users, becoming — for a while — the most-starred project on GitHub. You install it with one line, connect a channel, and start delegating from your phone. The workflow moves from pair programming to delegation and review. The interactive copilot asks, "What should I write next?" The autonomous agent asks, "What do you need done?" And that reframing is exactly why three questions now keep architects awake: Is it safe? You are handing a self-driving process the ability to run shell commands, touch files, and call APIs. One community report memorably described these agents as a teammate in your group chat who happens to have root access to your codebase. That is not a compliment — it is a threat model. Can it fit into real multi-agent work? A single agent is a demo. Production is a fleet — specialists that hand off to each other with gates in between. Is it flexible and controllable? Autonomy is thrilling right up until the agent packages last week's stale files into this week's deliverable, or loops forever on a failing test. This post answers all three — not with hand-waving, but with a working reference implementation you can clone today: CustomCodingAgentApp in the Multi-AI-Agents-Cloud-Native repo, an "Agentic Prototype Factory" that turns a plain-language idea into a tested, live-on-Azure prototype without leaving the chat window. A product manager types "Build a BBC-style World Cup feature page" in Microsoft Teams. Minutes later they get back a running HTTPS URL and a downloadable source ZIP. Under the hood, five specialized OpenClaw agents powered by Microsoft Foundry gpt-5.5 collaborate in a shared sandbox, run real pytest/Jest suites, and ship the result to Azure Container Apps — all orchestrated behind a Model Context Protocol (MCP) service so any MCP client (GitHub Copilot, Claude, the Teams bot) can drive it. We'll build up to that architecture in the order you should learn it. Part 1 — Long-running autonomous agents, and their two hard problems What actually makes them different A traditional chatbot is text in, text out. It waits for you. An autonomous agent inverts that: Property Traditional chatbot Long-running autonomous agent Execution Responds to a prompt Acts proactively (a "heartbeat" wakes it on a schedule) Scope Words Files, shell, browser, APIs — the real machine Memory This session only Persistent across sessions Interface A web box Any chat channel + the terminal Autonomy None Plans and takes multi-step action on its own Architecturally, OpenClaw is not a library you import — it's a runtime. A single long-running process (the Gateway) bridges your messaging channels to an LLM backend, keeps sessions alive, queues work in ordered lanes, and drives the classic agent loop: call the model → execute the tool calls it asks for → feed results back → repeat until done. There is no rigid step-planner; the model itself steers. That is what makes it feel magical — and what makes it hard to contain. That containment problem has two faces. Hard problem #1 — Security The same properties that make an autonomous agent useful make it dangerous. Full system access + proactive execution + a 32,000-server tool ecosystem is a large, self-driving attack surface. OpenClaw's own short history is the cautionary tale: a critical one-click remote-code-execution CVE early in its life, hundreds of malicious community "skills" discovered on its marketplace, and tens of thousands of gateways found exposed on the open internet. None of this means "don't use autonomous agents." It means: never run one with ambient credentials on a machine you care about. The agent belongs in a box with a hard wall around it. Hard problem #2 — Persistence and continuity Real agent work is long. Refactoring a codebase, researching across dozens of pages, building-testing-deploying an app — these take minutes to hours, far past a single request/response. So the runtime needs durable sessions, a place to keep state, and a workspace that survives across steps. But a persistent workspace that is reused creates its own hazard: state leakage. Files from yesterday's task can contaminate — or get shipped inside — today's result. Continuity and cleanliness pull in opposite directions, and you have to engineer the tension out. One agent is a demo; production is a fleet A single monolithic agent asked to "gather requirements, write the code, test it, deploy it, and package it" will do all four mediocrely and blur the boundaries between them. The production pattern is orchestrator-worker: specialized agents, each with one job, handing off to the next through explicit gates. OpenClaw supports exactly this — it can spawn sub-agents and even dispatch external coding harnesses, acting as a meta-orchestrator rather than a single model. The open question is never whether to go multi-agent; it's where the seams and the guardrails go. The answer to "is it safe?": put the agent in a microVM If the agent needs root to be useful, then give it root — inside a disposable microVM, not on your host. In 2026 there are several credible ways to do this: Kata Containers on AKS — each pod gets its own lightweight VM boundary and guest kernel. Hyperlight Wasm — per-call, snapshot-restored Wasm microVMs for running LLM-generated code. Azure Container Apps dynamic sessions — prewarmed, Hyper-V-isolated sandboxes that start in milliseconds, scale to thousands, and are purpose-built for "secure execution of custom code" and "running LLM-generated scripts." That last one — the ACA sandbox — is the sweet spot for a chat-driven agent factory: strong isolation without you operating a Kubernetes cluster, and an exec API to run commands inside the box. It's what the reference implementation uses. Part 2 — Putting OpenClaw into the ACA sandbox Here is where the repo stops being a diagram and becomes running code. The Agentic Prototype Factory decomposes the "idea → live app" job into five specialized OpenClaw agents that run in sequence, all inside the sandbox: requirements → coding → testing → deployment → save Each is addressable as its own model target on the OpenClaw gateway's OpenAI-compatible API: model value Routes to openclaw / openclaw/default Default agent openclaw/requirements-agent Requirement Agent openclaw/coding-agent Coding Agent openclaw/testing-agent Testing Agent openclaw/deployment-agent Deployment Agent openclaw/save-agent Save & download Agent Control, not vibes: review gates with feedback loops Autonomy without gates is how you get an agent that confidently deploys a broken app. The orchestrator wires the five agents into a graph with hard, bounded gates: Every knob is explicit and lives in server.py: _MAX_TEST_ROUNDS = 3, _MAX_DEPLOY_REVIEW = 2, _DEPLOY_POLL_ATTEMPTS = 12, _DEPLOY_POLL_DELAY_S = 20. The Testing Agent must end each turn with a literal TESTS_PASSED / TESTS_FAILED verdict; the orchestrator won't declare success until it HTTP-checks the deployed URL and inspects the response body — because a ResourceNotFound can happily return an HTTP 200. That is what "flexible and controllable" looks like in practice: the LLM drives creatively inside a deterministic state machine. The deterministic pre-run wipe (solving state leakage) Because the sandbox is reused across runs (fast, cheap), the orchestrator does something disciplined before every run: it wipes all lingering agent workspaces. Stale files from a previous task can never leak into — or be packaged as — the new result. This is the engineered answer to Hard Problem #2. Working with the sandbox's limits, not against them The ACA sandbox exec API is hard-capped at ~120 seconds — shorter than a cold az acr build plus az containerapp create. A naive agent would time out and report failure. The clever bit: those commands finish server-side on Azure even after the client exec disconnects. So deployment is split in two: deploy-build <dir> <app> — installs the deploy helpers, writes a tight .dockerignore, and kicks off the ACR build tagged <app>:latest. If the client drops at ~120s, the image still lands in ACR. deploy-finish <app> — idempotent, polled up to 12×. It reports STILL_BUILDING until the image exists, then fires a --no-wait containerapp create, and finally returns DEPLOYED_URL=https://<fqdn>. This is the single most important lesson of the whole sample: an autonomous agent doesn't need a longer timeout — it needs to understand the durability semantics of the platform it runs on. Part 3 — MCP, and why its security is the whole ballgame The five-agent workflow is powerful, but it would be a silo if the only way to reach it were a bespoke API. Instead, the repo wraps the entire orchestration as a Model Context Protocol (MCP) service (acamcp_node) exposed over streamable HTTP at /mcp, with a tiny, legible tool surface: MCP tool What it does generate_prototype Run the full five-agent workflow end to end run_agent Invoke a single named agent check_gateway_health Liveness / readiness of the OpenClaw gateway The payoff is enormous: any MCP client can now drive the factory — GitHub Copilot, Claude, or the Teams bot we're about to meet. One protocol, many front-ends. But MCP is not just an integration convenience — it's a control plane, and every MCP tool is a privileged capability. In an ecosystem with 32,000+ community servers, "just add an MCP server" is a supply-chain decision. A tool call is code execution by another name. So the security posture has to be deliberate. Here is how the reference implementation hardens it — and the principles are portable to any MCP deployment: Auth in front of the protocol. The MCP ingress sits behind basic auth (MCP_BASIC_AUTH_PASSWORD); the gateway itself requires the gateway token as a bearer credential (Authorization: Bearer <token>). No anonymous tool calls. A tiny, named allowlist — not a blank check. The gateway routes only to six explicit model targets. There is no "run arbitrary agent" escape hatch; the routing table is the allowlist. No secrets in the workload. There are no model API keys anywhere in the running containers — model access is brokered entirely through Entra ID managed identities. The gateway token is stored as a Kubernetes secret and never baked into an image. Private by default. The gateway's OpenAI-compatible endpoint is operator-level access — it stays on private ingress, with TLS and authentication added before anything is ever exposed publicly. Least privilege at the identity layer. The gateway is granted exactly the Foundry roles it needs (Cognitive Services User / Cognitive Services OpenAI User) on the Foundry resource — nothing more. The takeaway for MCP is the same as for the agent itself: treat the protocol as a doorway, and put a guard on the door. Authentication, an explicit allowlist, private ingress, and brokered identity turn MCP from an open blast radius into a governed control plane. Part 4 — The complete solution: Teams + MCP on ACA + OpenClaw on the ACA sandbox Now assemble the three deployable components into one loop: The request lifecycle, end to end A PM sends one sentence in Teams. The teamsbot_app bot — acting as an MCP client via mcpClient.ts — opens an MCP handshake and calls generate_prototype. The MCP service on ACA (acamcp_node) runs the orchestrator: pre-run wipe, then requirements → coding → testing. The OpenClaw gateway in the ACA sandbox (acasbxapp_node) executes each agent, talking to Foundry gpt-5.5 through a managed identity — no keys in the box. Real pytest + Jest suites run inside the sandbox. Fail → loop back (bounded). Pass → deploy. Deployment uses the build + poll split to survive the ~120s exec cap; the app lands in Azure Container Apps and is health-checked body-aware at its live URL. The Save Agent produces an authenticated ZIP download URL. The bot streams each agent's progress back into the Teams thread and returns the running HTTPS URL + source ZIP — optionally auto-opening the project in VS Code Insiders. How the architecture answers the three questions The question How this solution answers it Is it safe? The autonomous agent runs in a Hyper-V-isolated ACA sandbox, not on anyone's laptop. No model keys in the workload — Entra ID managed identity brokers Foundry. MCP behind basic auth; gateway behind a bearer token on private ingress; token as a secret, never in an image. A deterministic pre-run wipe removes cross-run leakage. Does it fit multi-agent work? It is a multi-agent system — five specialist OpenClaw agents with A2A hand-offs and review gates — and because it's exposed via MCP, any client (Copilot, Claude, Teams) can orchestrate it. Is it flexible and controllable? Creativity lives inside a deterministic state machine: explicit TESTS_PASSED/FAILED verdicts, bounded retry loops (_MAX_TEST_ROUNDS, _MAX_DEPLOY_REVIEW), body-aware health checks, and a human approving in the Teams thread. Deploy it yourself The repo ships scripts for all three tiers (the gateway uses the platform's managed identity to reach Foundry — no key handling, no image rebuild): # 1) OpenClaw gateway + the 5 agents (acasbxapp_node) cd acasbxapp_node cp .env.example .env # gateway token, Foundry endpoint, sandbox ids ./scripts/build-openclaw-image.sh # build + push the OpenClaw image to ACR ./scripts/deploy-aks-gateway.sh # grant Foundry roles + deploy # 2) MCP service (acamcp_node) cd ../acamcp_node cp .env.example .env # ACR + cluster; gateway token read from ../acasbxapp_node/.env ./scripts/build-images.sh # build + push the MCP image ./scripts/deploy-aks.sh # secret + manifests to the openclaw namespace ./scripts/smoke-check.sh # verify the MCP handshake # 3) Teams bot (teamsbot_app) — Node.js/TypeScript MCP client cd ../teamsbot_app # configure + run per the folder README, then sideload the Teams app package The reference implementation targets Azure (ACA + AKS) — the OpenClaw gateway and MCP service run as containers, and the code-execution sandbox uses the ACA dynamic-sessions exec API. Keep the gateway on private ingress and add TLS before any public exposure. Final thought Strip away the World Cup demo and a reusable pattern remains — a blueprint for running any long-running autonomous agent in the enterprise: A message-driven agent (OpenClaw / Hermes) + a microVM sandbox (Azure Container Apps dynamic sessions) + an MCP control plane with auth + enterprise identity (Entra ID managed identity) + a human surface (Microsoft Teams). The autonomy that made these agents go viral is the same autonomy that makes security teams nervous. You don't resolve that tension by slowing the agent down — you resolve it by giving it a box with a hard wall, a control plane with a guard on the door, an identity instead of a secret, and a human in the loop. Do that, and "your PM types a sentence, Azure ships an app" stops being a scary demo and becomes something you can actually put in production. Clone it, break it, harden it further: kinfey/Multi-AI-Agents-Cloud-Native → code/CustomCodingAgentApp The chat window is the new terminal. Let's make it a safe one.Token Economics: The New FinOps for Agentic AI
In AI applications, tokens are now cost — and token economics deserves architectural attention For a long time, AI application design started with model capability: Can the model write code? Can it reason? Can it use tools? Can it handle long context? Those questions still matter, but in the age of agentic applications, they are no longer sufficient. The more important production question is this: How many tokens does the architecture burn to complete one useful task? A classic chat application often maps one user turn to one model call. An agentic system is different. One user goal can trigger planning, retrieval, tool selection, tool execution, result interpretation, reflection, repair, and summarization. The user sees one instruction; the system may execute dozens of model calls behind the scenes. Tokens are no longer just a measure of text length. They become a measure of system design, runtime behavior, developer workflow, and business cost. GitHub Copilot’s 2026 move to usage-based billing through GitHub AI Credits captures the industry shift clearly. Usage is now aligned with token consumption, including input, output, and cached tokens. That matters because Copilot has evolved from an in-editor assistant into an agentic platform that can handle long, multi-step coding sessions across repositories. In that world, a tiny prompt and a multi-hour autonomous coding workflow should not be treated as the same economic unit. Token economics is therefore not about telling developers to “write shorter prompts.” It is about designing systems where: useful context is preserved, while noise is removed; repeated context is cached or deduplicated; simple tasks do not pay for frontier models; short-term state is managed structurally instead of copied repeatedly; every model call is metered, comparable, and governed. In short: token economics is the practice of making agentic AI economically sustainable. Scenario thinking: GitHub Copilot billing, Copilot SDK, GPT-5.5, Anthropic, and MAI-Code Model The new GitHub Copilot billing model provides a useful framing for developers. Copilot is no longer only autocomplete. It is becoming a programmable agentic platform. It can use models, call tools, work across files, stream responses, and participate in long-running coding workflows. With the GitHub Copilot SDK, developers can embed that agentic runtime into their own applications, services, and developer tools. That is powerful, but it also changes the cost model. Once an agent loop becomes programmable, token cost also needs to become programmable. If a system can plan, call tools, edit files, retry, repair, and summarize, it also needs to meter, route, cache, compress, and evaluate. EvalAgentic gives this idea a concrete playground. The project groups models into cost and capability tiers: Tier Example models Example price / 1K tokens Typical use LARGE claude-opus-4.8, gpt-5.5 $0.030 Agents, code generation, multi-step reasoning MID gpt-5.4-mini $0.012 Dialogue, summarization, extraction TINY gpt-5-mini $0.001 Classification, keyword matching, rule-like tasks This tiering lets us reason about real scenarios: GPT-5.5-class models are valuable for hard reasoning and engineering workflows, but they should not be the default for every step. Using a frontier model for simple classification is like hiring a principal architect to label folders. Anthropic high-capability models can be excellent for complex reasoning and coding, but they benefit from routing discipline. Requirements analysis, test interpretation, deployment explanation, and code generation may not need the same model tier. MAI-Code Model-style coding models should be treated as specialized capability layers. Their value is not just “better code generation”; it is deciding when code-specialized intelligence should be invoked in a larger agent pipeline. The real question is not “Which model is the best?” It is: Which model is the most economical and reliable for this step of this workflow? Four engineering techniques for saving tokens Context Compression: turn long text into executable structure Implementation principle Context Compression converts long natural-language context into the structured information an agent actually needs. Business documents are often verbose: resumes, contracts, product manuals, requirements, and support logs contain narrative text, boilerplate, repeated explanations, and low-value context. The next agent step may only need a few fields. EvalAgentic demonstrates this with a long resume-like input that is compressed into a compact JSON object. Instead of injecting the full original text into every prompt, the system extracts key fields and dynamically injects only the data required by the current task. A practical compression pipeline includes: Redundancy detection — identify long-tail text, repeated descriptions, stale history, and low-value context. Structured extraction — use Copilot or a mid-tier model to transform prose into JSON, tables, or typed schemas. Dynamic injection — inject only the fields needed for the next step. Recoverable references — preserve source pointers so compressed context remains auditable. How to evaluate Prompt token reduction before and after compression. Answer quality and task success rate. Schema fidelity and missing-field rate. Latency improvement. Cost per successful task. Compression is not summarization. Summaries are designed for humans. Structured compression is designed for agents. Prompt Deduplication / Cache: stop paying twice for the same context Implementation principle Many agent systems waste tokens because they repeatedly send the same context. The same resume, contract, repository README, user profile, API documentation, or business rule can be copied across turns and agents. Prompt Deduplication / Cache applies a simple principle: if context has already been processed, do not pay to process it again unless it has changed. A concrete design includes: compute a hash or semantic key for source context; reuse extracted structured results when content is identical or equivalent; apply a TTL for repeated entities, such as the 24-hour cache pattern shown in EvalAgentic; organize stable prompt prefixes to benefit from provider-level prompt caching where available; store shared context in an artifact store or memory layer so multiple agents do not copy the same blob. How to evaluate Cache hit rate. Cached token ratio. Duplicate prompt rate. Cost delta before and after caching. Correctness under cache, especially stale-cache failures. Caching is not “save everything forever.” Good caching knows when to reuse and when to invalidate. On-Demand Model Routing: let task complexity decide model tier Implementation principle On-Demand Model Routing routes each request to the cheapest model that can complete the task reliably. The entry point can use a rule tree, a lightweight classifier, or a hybrid complexity score. EvalAgentic’s routing tree is intentionally easy to explain: INCOMING REQUEST └─ Prompt < 500 tokens? ── YES ─→ TINY: classify / extract └─ NO ──→ multi-step reasoning? ├─ NO ─→ MID: dialogue / summary └─ YES ─→ LARGE: agent / code The engineering logic is straightforward: simple classification and keyword matching go to TINY; summarization and structured conversion go to MID; multi-step reasoning, coding, cross-file changes, and orchestration go to LARGE; code-specialized models such as MAI-Code Model can be placed in the coding phase rather than used across the whole pipeline. How to evaluate Routing accuracy. Cost per route. Quality regression by tier. Escalation rate from small models to larger models. End-to-end success rate. Routing does not mean “always use the smallest model.” It means frontier intelligence is reserved for the steps where it actually changes the outcome. Short-term Memory: preserve state instead of replaying history Implementation principle Short-term Memory controls context growth across multi-turn and multi-agent workflows. Without it, agents often replay the full conversation history, full tool outputs, and full intermediate reasoning on every turn. The context grows; quality may not improve; the bill definitely does. A better design stores state structurally: user goal; current plan; tool outputs and references; failure reasons; next actions; handoff artifacts between agents. In a multi-agent coding pipeline, the Requirements Agent should hand off a structured spec. The Coding Agent should read that spec, not the entire prior conversation. The Testing Agent should consume testable artifacts, not every word produced by the Coding Agent. How to evaluate Context growth curve across turns. Memory retrieval precision. Rework rate caused by missing state. Recovery quality after failed steps. Average input tokens per turn. Short-term memory is not about remembering everything. It is about remembering the next useful thing. EvalAgentic as a concrete evaluation example EvalAgentic is effective as an evangelism project because it turns token economics into an observable before/after system. The architecture has five layers: Frontend — frontend/index.html provides Tabs A / B / C, live SSE logs, and before/after charts. API — backend/server.py exposes FastAPI routes and Server-Sent Events streaming. Orchestration — eval.py handles A/B evaluation; coding_agents.py handles the multi-agent coding scenario. Core — compressor.py, router.py, gh_models.py, and token_meter.py implement compression, routing, Copilot SDK calls, and token metering. Providers — GitHub Copilot SDK and Microsoft Agent Framework provide model access and agent orchestration. Tab A: Compression comparison Tab A compares long-form context before and after structured compression. The key message is that token saving does not come from writing a clever sentence. It comes from converting verbose context into a structured artifact that downstream agents can consume efficiently. Tab B: On-demand model routing Tab B demonstrates that cost is not only about raw token count. If a system routes simple tasks to cheaper tiers and reserves expensive models for complex reasoning, total cost can fall even if some token counts increase. This is a subtle but important point: token economics is not token starvation; it is model portfolio optimization. Tab C: Coding scenario — multi-agent with Agent Framework Tab C is the most persuasive demo. The same deliverable — a Taobao-like goods-list site with HTML + JavaScript frontend, Flask backend, and Docker deployment — is produced twice by a four-agent pipeline: Requirements Agent; Coding Agent; Testing Agent; Deployment Agent. The before pipeline uses no compression and sends every agent to GPT-5.5 / LARGE. The after pipeline injects a compressed JSON spec and uses on-demand routing: requirements can use MID, coding can use LARGE, testing can use MID, and deployment can use TINY. This mirrors real enterprise development. Architecture and complex code generation may deserve frontier models. Test interpretation, deployment packaging, and simple validation often do not. Summary and refinement based on the project diagrams The EvalAgentic README describes three important visuals: the architecture flow, the routing tree, and the token-meter design. Together, they form a governance loop: User Scenario ↓ Context Compression ↓ Prompt Deduplication / Cache ↓ On-Demand Model Routing ↓ Short-term Memory ↓ Token Metering & Budget Actions ↓ Before / After Evaluation Optimize the path, not only the prompt Many teams start token optimization by editing prompt wording. That helps, but the largest waste usually lives in the execution path: how many calls are made, how much context is repeated, how often tools retry, and whether every step uses the same expensive model. EvalAgentic makes the path visible through A/B comparisons. Token Meter is the control plane of cost governance EvalAgentic’s token_meter.py uses a non-invasive interceptor pattern: INTERCEPTOR (@token_meter) ↓ COUNTER CORE: accounting / budget threshold / trigger ↓ ACTION HUB: throttle (>80% budget) / rollback (>budget) This is the right architectural instinct. Production systems need thresholds, throttling, rollback, and traceability. Without those controls, one retry loop can quietly turn a small user request into a budget incident. Cost metrics must be evaluated with quality metrics A system that cuts cost by 80% but drops success rate by 50% is not optimized. It is broken more cheaply. The evaluation matrix should combine cost, quality, latency, and reliability: Dimension Metric Why it matters Cost Cost per successful task Measures the real unit economics Token Input / output / cached tokens Identifies compression and cache opportunities Quality Pass rate / regression rate Ensures cheaper tiers do not break outcomes Efficiency Latency / retry count Prevents cheap models from causing expensive retries Governance Budget breach / rollback count Validates runtime control Narrative A simple three-line narrative works well for demos: Token is no longer a technical detail. It is the bill of your architecture. EvalAgentic shows the same scenario before and after cost-aware design. The goal is not to make models cheaper; the goal is to make agent systems economically governable. For a developer audience, the sharper version is: A good agent does not use the biggest model everywhere. It uses the right intelligence at the right step, with the right context, under the right budget. Practical recommendations for real projects Establish a token baseline first. Measure input, output, retries, tool calls, and cost per scenario before optimizing. Make compression a component, not a prompt habit. Define schemas, cache policies, and fallback behavior. Introduce a model routing matrix. Route by task type, complexity, risk, latency, and cost. Define handoff contracts between agents. Pass structured artifacts, not endless conversation history. Evaluate every optimization with A/B tests. Compare cost, quality, latency, and stability. Add budget actions. Throttle at a threshold, rollback on breach, and add circuit breakers for failed retries. Closing: token economics is the second curve of agent engineering The first phase of AI application development was about calling models. The second phase was about putting models into products. The next phase of agentic AI is about running those systems reliably, affordably, and governably. EvalAgentic matters because it turns Context Compression, Prompt Deduplication / Cache, On-Demand Model Routing, and Short-term Memory into something developers can run, compare, and explain. It moves token economics from opinion to instrumentation. Future AI applications will not only ask: How smart is this agent? They will ask: How many tokens does it spend per completed task? Which model did it use? Did it hit cache? Did retries run away? Did the system reserve frontier intelligence for the steps that deserved it? References kinfey/EvalAgentic GitHub Copilot is moving to usage-based billing Updates to GitHub Copilot billing and plans Copilot SDK - GitHub Docs
