Engineering Agentic Recall Controls with MCP and Microsoft Foundry
AI agents become operationally interesting when they can reach real systems. They also become operationally dangerous at exactly the same moment. Caldova Recall Control Tower is a developer demonstration built around that tension. It uses a fictional pharmaceutical recall to show how an agent can gather evidence and prepare a decision while deterministic application code retains authority over approval and inventory mutation. The implementation combines the Model Context Protocol (MCP), Microsoft Agent Framework, a Microsoft Foundry Hosted Agent, FastAPI, Microsoft Entra authentication, managed identity, and optimistic concurrency in Azure Blob Storage. Central design rule: Let the model interpret and recommend. Make ordinary code authenticate, authorize, mutate, and prove what happened. Caldova is fictional, all operational data is synthetic, and this sample is not a production recall system or a source of clinical advice. The scenario: useful reasoning, consequential action The demo starts with a temperature excursion affecting batch B-2408-AX7 of Caldova Relief 20 mg tablets. The synthetic inventory contains 2,196 units across two distribution centers and two retail stores. A useful system must establish the notice, locate every affected position, check supplier status, explain uncertainty, and recommend an action. That analysis is a good fit for specialized agents. Quarantining inventory is not. Quarantine changes operational state. It therefore needs an authenticated human, explicit authorization, a batch-scoped approval, concurrency control, idempotency, and an audit record. None of those guarantees should depend on a prompt being followed. The authenticated hosted application at the start of the fictional recall. Architecture: separate reasoning from authority The solution has two related but deliberately separate paths. Caldova separates model reasoning from application authority. Official Microsoft service icons identify Foundry Agent Service, App Service, Managed Identity, and Blob Storage. The reasoning path invokes a Hosted Agent through the Responses protocol. Four agents run in a fixed sequence: triage, inventory impact, supplier/compliance, and supervisor. The first three have narrow read-only tools. The supervisor has no tools and synthesizes the accumulated context into a decision brief. The authority path remains in the web application. It validates the EasyAuth identity claims, checks an approver allowlist, binds approval to the caller, batch, action, and current demo generation, and only then calls deterministic domain code. State is stored per actor in Blob Storage and updated with ETag match conditions so concurrent writes fail instead of silently overwriting each other. There is also a deterministic localhost demo. It reuses synthetic domain fixtures and demonstrates MCP contracts, approval, quarantine, and replay without a model or cloud account. It is useful for development, but its typed approver name and in-memory state are not production identity or durable compliance evidence. Building a narrow MCP surface MCP standardizes how an AI application discovers and calls external tools. It does not remove the need to design those tools carefully. Caldova exposes small, typed operations such as get_recall_notice , locate_inventory , and get_supplier_status . Inputs are constrained with Pydantic, and tool annotations tell clients that these operations are read-only and closed-world: @mcp.tool( title="Locate affected inventory", annotations=ToolAnnotations( read_only_hint=True, open_world_hint=False, ), ) def locate_inventory(batch_id: BatchId) -> dict[str, Any]: return STORE.locate_inventory(batch_id) The mutation tool is separately marked destructive and requires a batch-scoped approval token. Those annotations improve discovery and planning, but they are metadata, not an authorization boundary. The real check occurs inside quarantine_batch , below the model and below the tool description. The Hosted Agent does not receive the mutation tools at all. Its specialists call only three read operations through an isolated MCP stdio subprocess. This is stronger than asking an all-powerful agent to "please remain read-only": capability is constrained by construction. The subprocess boundary also keeps MCP v2 dependencies isolated from the Foundry hosting environment. Each call has a timeout, bounded concurrency, structured JSON handling, and a generic failure response that does not leak subprocess details. Fixed workflows beat vague autonomy for this case Multi-agent does not have to mean dynamic routing. Caldova uses an explicit sequence because the business dependency is explicit: validate the notice before locating inventory, locate inventory before checking supplier implications, and synthesize only after all three specialist outputs exist. return ( WorkflowBuilder(start_executor=triage, output_from=[supervisor]) .add_edge(triage, inventory) .add_edge(inventory, compliance) .add_edge(compliance, supervisor) .build() .as_agent() ) This topology is easier to test and reason about than an unconstrained planner. Each specialist has one job and one tool allowlist. Full context is passed where synthesis requires it, while the supervisor remains tool-free. Request isolation matters too. A hosted process can serve concurrent users, so workflow state must not leak between requests. The sample creates a fresh workflow agent for each request context rather than reusing mutable agent state globally. What the live hosted run showed The hosted application completed a read-only assessment for the synthetic batch. The resulting brief reported: 2,196 affected units across four locations. A high-risk inbound temperature excursion. Supplier acknowledgement, a 36-hour replacement estimate, and a drafted credit note. Unknown transit temperature details, excursion duration, stability impact, final supplier disposition, and potentially issued stock. A recommendation to hold or quarantine stock, explicitly stating that no quarantine had occurred. A required human approval before any inventory restriction. The live Hosted Agent decision brief. Transient response and correlation identifiers are masked; the operations rail is excluded because it contains actor-scoped audit data. The screenshot also shows an important truthfulness choice: the UI says Hosted workflow trace unavailable. The application does not invent stage completion or tool-call evidence when the hosted endpoint does not return trustworthy trace data. The answer can be displayed, but it must not be presented as proof of an internal execution path. Approval is a protocol, not a button The hosted web path uses App Service authentication with Microsoft Entra. The application accepts the injected principal only on the configured App Service host, validates tenant and object identifiers, applies a user allowlist, and performs an additional approver check for mutation requests. State-changing calls also require the expected origin and an application request header. Approval is then bound to five facts: The authenticated actor. The current demo generation. The affected batch. The quarantine action. A ten-minute validity window until first use. Resetting the demo creates a new generation, invalidating old handles. Consuming an approval does not make replay unsafe: the same bound handle can repeat the same quarantine operation, but domain code changes only positions that are not already quarantined. A second call reports an idempotent replay with zero additional positions changed. This is the difference between a human-in-the-loop interface and a human-authorized system. A modal dialog provides user experience; identity binding and deterministic policy provide control. Durable state needs concurrency semantics The hosted application stores each actor's synthetic session in a separate Blob object. A load returns both JSON state and its ETag. A save uses IfNotModified semantics; if another request updated the same state first, Azure Storage rejects the stale write and the API returns a conflict. conditions = ( {"etag": etag, "match_condition": MatchConditions.IfNotModified} if etag else {} ) await blob.upload_blob( json.dumps(state), overwrite=etag is not None, **conditions, ) Without that condition, two browser requests could both read the same approval state and overwrite one another using last-writer-wins behavior. Agent systems do not get a concurrency exemption: ordinary distributed-systems rules still apply. Fail closed, and make the failure legible The analysis adapter accepts only HTTPS Foundry endpoints with the expected path, uses a managed-identity token for https://ai.azure.com/.default , disables redirects, and enforces bounded connect and overall timeouts. It accepts only a completed assistant response with non-empty output text. If the endpoint times out, returns partial output, returns malformed data, or becomes unavailable, the application clears the analysis lease and reports that no inventory changed. It does not substitute a local answer and label it as hosted. Approval remains locked until a new hosted analysis succeeds. This can feel strict during a demo, but it protects provenance. A degraded fallback is useful only when the UI and audit model can identify it accurately. What is proven, and what is not The sample provides useful evidence for several engineering claims: Typed MCP tools reject malformed input. Hosted specialists receive read-only capabilities only. Approval is checked below the model and bound to identity and session state. Quarantine is idempotent in the synthetic domain. Blob ETags prevent stale session writes. Empty, partial, failed, and timed-out hosted responses fail closed. Local tests cover domain, MCP, workflow, API, and repository-hygiene behavior. It does not prove that the sample is a production recall platform. The scenario is synthetic. The local audit log is not tamper-evident. The hosted UI currently lacks trustworthy per-stage and per-tool trace rendering. Deployment-specific RBAC, EasyAuth configuration, telemetry access, model behavior, load characteristics, costs, and recovery procedures require validation in each environment. Evaluation evidence also expires. Golden cases and evaluator configuration are useful assets, but historical results are not a current release certificate. Re-run evaluations against the deployed agent version and inspect failures before making quality claims. Try the pattern Start with the deterministic path before provisioning cloud resources: Set-Location (git rev-parse --show-toplevel) py -3.13 -m venv caldova-recall-control/.venv ./caldova-recall-control/.venv/Scripts/python.exe -m pip install ` -r caldova-recall-control/requirements-ui.txt ./caldova-recall-control/.venv/Scripts/python.exe -m uvicorn ` control_tower_api:app ` --app-dir caldova-recall-control/src ` --host 127.0.0.1 ` --port 8091 Then inspect the MCP server over stdio: ./caldova-recall-control/.venv/Scripts/python.exe ` caldova-recall-control/scripts/inspect_mcp.py The inspector discovers the real tool schemas, reads the synthetic inventory, rejects malformed input and an unapproved mutation, and confirms the stock remains unchanged. Only after that local contract is understood should you configure a Foundry project, deployment identity, model deployment, and Hosted Agent. Engineering takeaways The most reusable lesson in Caldova is not the number of agents. It is the placement of authority. Give each model the smallest useful toolset. Prefer explicit workflow topology when the business process is known. Treat tool annotations as descriptive metadata, not access control. Bind consequential approval to authenticated identity, resource, action, and session generation. Put mutation and idempotency in deterministic domain code. Use optimistic concurrency for durable web state. Preserve provenance by failing closed instead of silently changing execution paths. Show only evidence the system actually captured. Agents are excellent at turning fragmented evidence into an actionable brief. Reliable systems make sure the brief and the action remain two different things. References Caldova source repository Model Context Protocol introduction Microsoft Agent Framework workflow capabilities Deploy a Hosted Agent in Microsoft Foundry Configure Microsoft Entra authentication for Azure App Service Manage concurrency in Azure Blob Storage
Defender streaming setup and integration with private event hub
Hey team, Do we know Defeneder supports private endpoint only event hubs to ingest data - I think only public is supported Configure Azure Event Hubs for Microsoft Defender XDR event ingestion - Microsoft Defender XDR | Microsoft Learn Could anyone please clarify?
Building a Fully Automated Azure Landing Zone Deployment Using Azure DevOps and Terraform
Discover how to build a fully automated Azure Landing Zone using Azure DevOps and Terraform. This article walks through real-world Git workflows, CI/CD automation, environment promotion strategies, and governance integration to create secure, scalable, and enterprise-ready Azure environments.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 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 ServiceVector 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.Building and Deploying Microsoft Hosted Agents to Microsoft Teams
A practical, engineer-to-engineer guide to taking an AI agent from a developer laptop, into Microsoft Foundry Agent Service, and out to end users inside Microsoft Teams and Microsoft 365 — using the BRK241 FibreOps reference implementation as a worked example. Introduction: the hard part is no longer building the agent Two years ago, wiring an LLM to a couple of tools felt like the summit. It isn't any more. Frameworks, hosted models, and function-calling have made the build step almost routine. The problem has quietly moved downstream. The genuinely hard questions today are operational: Where does the agent run when it's no longer on your machine? What identity does it use to call enterprise systems, and who granted it? How does a platform team scale, monitor, and roll it back? How do business users actually reach it without learning a new tool? Who signed off on it touching production data? A prototype answers none of these. A production agent platform answers all of them, repeatably, for every agent an organisation ships. That shift — from a clever notebook to a governed, observable service that lands in the tools people already use — is the subject of this article. We'll use a single narrative to keep it concrete: FibreOps, the BRK241 "Autonomous Fibre Outage Response" system. It ingests optical line terminal (OLT) telemetry, analyses incidents, files tickets in Dynamics 365 Field Service, posts Adaptive Cards to Microsoft Teams, and dispatches engineers — all through role-specialised agents. The full source is on GitHub. The story runs on three verbs: Build → Run → Distribute. Section 1: Building the agent An agent is not one mega-prompt. FibreOps is deliberately factored into three role-specialised agents behind a single orchestrator, each with its own tool surface, its own system instructions, and a strict output contract: IncidentAnalysisAgent — classifies severity, finds probable cause, and pulls the correct standard operating procedure (SOP). NetOpsCoordinatorAgent — files the D365 incident and posts the Teams outage notice. FieldDispatchAgent — selects the best engineer by skill, region and shift, books the resource, and updates Teams. The Coordinator hands off to Dispatch with a literal HANDOFF:DISPATCH token rather than a fuzzy "I think we should…". Hard contracts between agents are how you stop them inventing work. Microsoft Agent Framework The agents are built with the Microsoft Agent Framework (MAF). The key design decision in the reference implementation is that all three backends honour one contract — await agent.run(prompt) -> response — so the orchestrator never knows or cares where reasoning actually happens: local — a deterministic LocalAgent shim with no LLM, so the demo runs with zero Azure credentials. foundry — agent_framework.Agent + FoundryChatClient , definition resolved locally. Ideal while iterating on prompts. hosted — agent_framework_foundry.FoundryAgent bound to a Prompt Agent published to Foundry Agent Service. This is the production path. Building a Foundry-backed agent is just a client plus instructions plus typed tools: from agent_framework import Agent from agent_framework_foundry import FoundryChatClient from azure.identity import DefaultAzureCredential client = FoundryChatClient( project_endpoint=settings.azure_ai_project_endpoint, model=settings.azure_ai_model_deployment, # e.g. gpt-4.1-mini credential=DefaultAzureCredential(), # no connection strings, ever ) agent = Agent( client=client, instructions=INCIDENT_ANALYSIS_INSTRUCTIONS_V1, name="IncidentAnalysisAgent", tools=[lookup_sop, recall, remember, web_iq_search, work_iq_search], ) Note the DefaultAzureCredential . There are no keys or connection strings anywhere in the reasoning path — identity flows from Microsoft Entra ID. Keep that in mind; it becomes the backbone of the governance story later. Tool calling and MCP Every tool is a typed Python function. Foundry sees the JSON schema derived from the signature; the runtime executes the Python. That separation matters: the published agent definition stores only the model and instructions, while the implementations are supplied by the runtime on every call. The same in-process tools (Teams, D365, dispatch, knowledge, memory) run identically whether the agent is local or hosted. Beyond your own functions, Foundry agents can draw on hosted toolbox tools ( web_search , code_interpreter ) and Model Context Protocol (MCP) servers. MCP is the open standard for exposing tools, resources and prompts to agents over a uniform protocol, so an enterprise can stand up an MCP server once and let every agent consume it. In FibreOps this is config-gated — set FIBREOPS_FOUNDRY_TOOLBOX=1 and the incident analyst gains live web search alongside its Web IQ / Work IQ connectors, with no code change. Grounding strategies FibreOps grounds reasoning three ways, in layers: Retrieval over owned knowledge — SOPs (markdown) and the fibre-node topology graph, looked up by the analysis agent. Foundry IQ — Web IQ for public context (roadworks, weather, power) and Work IQ for enterprise context (site surveys, SLA tiers, competency matrix). Procedural memory — prior incidents for a node, recalled before analysis so the agent learns from history. Crucially, when the IQ endpoints are unset the tools fall back to deterministic fixtures so the agent always grounds. Grounding that silently fails is worse than no grounding; design your fallbacks explicitly. Local development, testing and evaluation The whole system runs from one command with no cloud dependency: # Deterministic local backend — no Azure credentials required python -m fibreops.demo --signals 3 --backend local Every run is persisted as a JSON document — the input signal, every agent step, every tool call, every output, every ticket. That single artefact shape feeds three consumers: structured logs, the local optimiser, and Foundry Evaluators. The optimiser scores each run against a five-criterion rubric (was the analysis complete, was severity consistent with customer impact, did a ticket land, did dispatch policy match severity, was an SOP cited) and writes back concrete improvement suggestions. That evaluation loop — not the first working demo — is what turns a prototype into a system you can keep improving. Section 2: Deploying to Microsoft Foundry Agent Service Microsoft Foundry Agent Service is the managed runtime that hosts your agents. It gives you a secure, isolated execution environment, an agent runtime that speaks the OpenAI-compatible Responses API, plus hosted memory, toolboxes, knowledge integrations, and observability — without you operating any of it. FibreOps demonstrates the two hosting shapes Foundry offers. Shape 1 — Prompt Agents A Prompt Agent stores a model deployment plus system instructions as an immutable, versioned definition in Foundry. Publishing is a one-time step per change: from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition from azure.identity import DefaultAzureCredential pc = AIProjectClient(endpoint=endpoint, credential=DefaultAzureCredential(), allow_preview=True) pc.agents.create_version( agent_name="fibreops-incident-analysis", definition=PromptAgentDefinition( model=model_deployment, instructions=INCIDENT_ANALYSIS_INSTRUCTIONS_V1, ), description="FibreOps incident analysis agent", ) At run time you bind to the published version with a FoundryAgent , and — as noted above — the runtime supplies the tool implementations. Prompt versioning ( instructions_v1 , _v2 , _v3 ) is where the optimiser's suggestions land, closing the improvement loop inside the platform. Shape 2 — Containerised hosted agents The BRK241 hero path packages the entire analyse → coordinate → dispatch flow as a single hosted agent: a container that serves the Responses /responses contract on port 8088, deployed straight into your Foundry project. The Agent Framework agent is wrapped by ResponsesHostServer : from agent_framework_foundry_hosting import ResponsesHostServer def main() -> None: server = ResponsesHostServer(build_system_agent()) # Foundry sets the reserved PORT env var inside the sandbox server.run(host="0.0.0.0", port=8088) The container is declared in agent.yaml — kind: hosted , the image reference, the per-session sandbox size (0.5/1 Gi, 1/2 Gi or 2/4 Gi), the protocol version, and only user-declared environment variables. You never hard-code FOUNDRY_* values or the Application Insights connection string; the platform injects those at run time. Deployment registers the image as an immutable version and polls until active : details = pc.agents.create_version( agent_name="fibreops-outage-response", definition=HostedAgentDefinition( protocol_versions=[ProtocolVersionRecord( protocol=AgentProtocol.RESPONSES, version="1.0.0")], cpu="1", memory="2Gi", container_configuration=ContainerConfiguration(image=image), environment_variables={"MODEL_DEPLOYMENT_NAME": model_deployment}, ), ) From local execution to managed hosting The migration path is deliberately gentle because the contract never changes. A developer iterates locally against LocalAgent , moves to the foundry backend to test real prompts, then publish es Prompt Agents or builds and deploy-hosted s the container. The orchestrator code is byte-for-byte identical across all three. That property — same code path local for dev, hosted in Foundry for prod — is the single most important thing to preserve when designing your own agents. Scaling, memory, toolboxes, knowledge and observability Scaling — Foundry provisions a per-session sandbox and a dedicated Entra agent identity per hosted-agent version; you size the sandbox in agent.yaml and let the platform handle isolation. Memory — set FOUNDRY_MEMORY_STORE_NAME and a FoundryMemoryProvider is attached as a context provider so agents read and write learned procedures in Foundry's hosted store; unset, they use local SQLite. No code change. Toolboxes & knowledge — hosted web_search , code interpreter, MCP, and Web/Work IQ connectors are curated per role and merged with your Python tools. Observability — the agent emits OpenTelemetry spans; set APPLICATIONINSIGHTS_CONNECTION_STRING (injected by the platform for hosted agents) and every agent decision, tool call and latency is queryable in Application Insights. Section 3: IT and development responsibilities Successful agent deployments need both developer velocity and platform governance. The failure mode at either extreme is familiar: developers who can't ship because every request routes through a ticket queue, or a free-for-all where nobody can say what identity an agent runs as. The workable model draws a clean line of responsibility. Concern Developer / Agent team IT / Platform team Identity Use DefaultAzureCredential ; never embed secrets; declare the scopes the agent needs Provision the managed / Entra agent identity; own the app registration and consent Access control Request least-privilege roles for the tools the agent calls Grant RBAC at the correct scope; run role-assignment scripts; enforce approvals Security Validate inputs, handle tool failures cleanly, avoid data exfiltration in prompts Disable ACR admin, enforce managed-identity pulls, network controls, Key Vault for secrets Compliance Keep decisions explainable and replayable (the JSON run record) Data-residency, retention, audit, Responsible AI review sign-off Monitoring Emit structured traces + OTel spans; define the rubric Own Application Insights / Log Analytics, alerting, dashboards, SLOs Cost Right-size the sandbox and model deployment; cache grounding Budgets, quota, token-consumption monitoring, chargeback Lifecycle Version prompts and images; feed the optimiser back into new versions Environment promotion (dev → test → prod), rollback, deprecation The reference implementation encodes this split honestly. The Bicep template does not create role assignments, because most deployers only hold Contributor . Instead a subscription Owner runs scripts/grant-mi-roles.ps1 once to grant the App Service's identity exactly the roles it needs — Event Hubs Data Owner, Key Vault Secrets User, AcrPull, Azure AI Developer, and Cognitive Services OpenAI User — and no more. That is least privilege made operational. Section 4: Publishing to Microsoft Teams and Microsoft 365 An agent nobody can reach has no value. The final verb — Distribute — puts the agent where users already work. FibreOps reaches Teams two ways. The lightweight path: Adaptive Cards via Incoming Webhook The NetOps coordinator posts outage notices and status updates to a Teams channel as Adaptive Cards through an Incoming Webhook. Any unconfigured channel is logged to state/teams_outbox.jsonl , so the same code runs in a demo and in production — you only change the webhook target. This is the fastest way to get agent output into Teams and is ideal for notifications and human-in-the-loop review. The rich path: a declarative agent for Microsoft 365 Copilot To make the agent conversational and discoverable across Teams, Microsoft 365 Copilot and copilot.microsoft.com, FibreOps ships as a declarative agent plus an API plugin action. One command builds the sideload-ready package: python -m fibreops.demo publish-m365 --out dist/m365 # wrote declarativeAgent.json (name, description, conversation starters) # wrote fibreops-action.json (API plugin -> {base_url}/openapi.json) # wrote manifest.json (Teams app manifest) # wrote color.png / outline.png (icons) # wrote fibreops-copilot.zip (upload this) The declarative agent declares metadata, conversation starters and a capability set; the action plugin proxies tool calls to the deployed FastAPI app via its OpenAPI document. Set M365_ACTION_BASE_URL to the app's public HTTPS root before publishing — the CLI warns when the placeholder is still in effect. That single environment variable is the only thing that flips the package from demo to production. The end-to-end distribution workflow Conceptually, the artefact travels a fixed pipeline: Developer laptop │ build + test (local backend) → publish Prompt Agent / deploy hosted container ▼ Microsoft Foundry Agent Service │ hosted agent, secure sandbox, Entra agent identity, observability ▼ Teams App package (fibreops-copilot.zip) │ Teams Admin Center → Manage apps → Upload (or M365 Admin Center → Integrated apps) ▼ Microsoft 365 tenant │ admin approval, availability policy, targeted rollout ▼ End user in Teams / M365 Copilot Enterprise rollout is rarely "publish to everyone". The realistic pattern is a staged one: sideload to a pilot group, gather feedback and optimiser scores, then widen availability through Teams app-permission and app-setup policies to department, then tenant. Because the package carries publisher metadata and the declarative schema, IT can review it exactly like any other line-of-business app. Section 5: Enterprise governance Governance is not a bolt-on; in this architecture it's a property of the platform. The pillars: Entra ID integration and agent identity — every hosted agent version gets a dedicated Entra agent identity. Nothing authenticates with a shared key. DefaultAzureCredential means the same code picks up a developer's identity locally and the managed identity in production. RBAC at the right scope — roles are granted to identities, not baked into images. Deploying a hosted agent requires Azure AI Project Manager at project scope; the Foundry project identity needs AcrPull on the registry to pull the container. Least privilege is enforced, not assumed. Auditability — the JSON run record plus OpenTelemetry spans in Application Insights give you a replayable, per-incident audit trail. You can reconstruct exactly which SOP was cited, which engineer was chosen, and why severity was escalated. Data boundaries — the mock D365 is a drop-in for a real Dataverse environment; grounding sources are enterprise connectors (Work IQ) kept inside the tenant boundary. Nothing leaves the subscription without an explicit connector. Responsible AI — the Adaptive Card JSON can be pasted into the Adaptive Cards designer for governance review; the evaluation rubric makes quality measurable; explicit grounding fallbacks prevent silent failure. Production readiness — immutable versioning, one-command rollback (delete a version), managed-identity-only image pulls, and disabled ACR admin credentials are all first-class in the reference deployment. Section 6: Reference architecture The following diagram shows the production topology — users on the left, enterprise systems and controls on the right, with Foundry Agent Service at the centre hosting the agent. flowchart LR User["NOC operator / business user"] subgraph M365["Microsoft 365 tenant"] Teams["Microsoft Teams(Adaptive Cards + declarative agent)"] Copilot["Microsoft 365 Copilot"] end subgraph Foundry["Microsoft Foundry Agent Service"] Hosted["Hosted AgentOutage Response System(secure per-session sandbox)"] Runtime["Agent runtime(Responses API)"] Memory["Hosted memory + toolboxes"] end subgraph Enterprise["Enterprise data & tools"] MCP["MCP servers / web_search"] D365["Dynamics 365 Field Service"] EventHub["Azure Event Hubs(OLT telemetry)"] Knowledge["SOPs + topology + Web/Work IQ"] end subgraph Ops["Cross-cutting"] Obs["ObservabilityApp Insights / OTel"] Gov["GovernanceEntra ID · RBAC · audit"] end User --> Teams User --> Copilot Teams --> Runtime Copilot --> Runtime Runtime --> Hosted Hosted --> Memory Hosted --> MCP Hosted --> Knowledge Hosted --> D365 EventHub --> Hosted Hosted -.->|Adaptive Cards| Teams Hosted --> Obs Gov -.->|identity & policy| Foundry Gov -.->|identity & policy| Enterprise Read the solid arrows as the control/orchestration flow and the dashed arrows as governance and outbound notifications. The point of the diagram is that governance (Entra ID, RBAC, audit) applies across every component, and observability captures every agent decision — neither is optional plumbing. Section 7: What production looks like Picture the FibreOps rollout at a national fibre operator, with the four personas doing their part: Developers build the three agents and the orchestrator on their laptops against the local backend — no cloud, no credentials, deterministic tests. They tune prompts against the foundry backend, watch the optimiser rubric climb from 0.90 to 1.0 as they add the ">5,000 customers ⇒ escalate to critical" rule, and commit a new instruction version. The platform team deploys the container to Foundry Agent Service via scripts/deploy-hosted-agent.ps1 , which builds the image in ACR, pushes it, and registers an immutable version. They provision the Event Hub, Key Vault, Log Analytics and Application Insights from Bicep, and size the sandbox at 1 vCPU / 2 GiB. IT approves the workload: a subscription Owner grants the managed identity its five least-privilege roles, hardens the App Service to pull via managed identity, disables ACR admin, and signs off the Responsible AI review using the replayable run records and the Adaptive Card previews. They sideload fibreops-copilot.zip to a pilot channel first. Business users consume it inside Teams. When an OLT in London loses light, an Adaptive Card appears in the NOC channel within seconds — severity, probable cause, ticket ID, and the dispatched engineer's ETA — with no human having read a dashboard, opened a ticket, or phoned a dispatcher. If Foundry ever wobbles, the same system falls back to the deterministic local agent with an identical trace shape. Every integration but D365 is live in the demo, and D365 is a one-variable swap to a real Dataverse endpoint. That is the whole point: the demo and production differ by configuration, not by code. Key takeaways Design for one contract. If agent.run(prompt) behaves identically local, foundry-backed and hosted, migration to production is configuration, not a rewrite. Factor agents by role with hard handoff contracts. Literal tokens like HANDOFF:DISPATCH beat fuzzy natural-language handoffs and stop agents inventing work. Never embed secrets. DefaultAzureCredential + Entra agent identities give you keyless auth that works the same everywhere. Make every run replayable. A single JSON artefact that feeds logs, evaluation and audit is worth more than any dashboard. Ground explicitly, and design your fallbacks. Grounding that fails silently is a liability; deterministic fixtures keep the agent honest. Split responsibility cleanly. Developers own velocity and quality; the platform team owns identity, scale, cost and promotion. Encode the split in scripts, not tribal knowledge. Version prompts and images immutably. Rollback should be "delete a version", and the optimiser's suggestions should land as the next version. Distribute where users already are. Adaptive Cards for notifications, a declarative agent for conversation and discovery across Teams and M365 Copilot. Roll out in stages. Pilot channel → department → tenant, gated by app policies and real optimiser scores. Resources Reference implementation: github.com/leestott/BRK241-frontier Microsoft Agent Framework overview Microsoft Foundry Agent Service Hosted agents in Foundry Agent Service · Deploy a hosted agent Microsoft Teams developer platform Declarative agents for Microsoft 365 Copilot Model Context Protocol GitHub Copilot Clone the repo, run python -m fibreops.demo --signals 3 --backend local , and watch the analyse → coordinate → dispatch loop close. Then wire in your own Foundry project and take it all the way to Teams. Go build something.
Question about Microsoft IT specialist certification, in relation to Coursera.
Please, bear with me, as it took me 3 hours just to get navigated here for help. I am taking IT courses with Coursera. I am not an IT professional...I am trying to change my career and my life. I was taking Googles IT Support Specialist course, but after 4 days of classes, I came across a major roadblock: Outdated information on their QwikLabs setup. The course was at least a year behind, the Demo on how to use Qwiklabs was seriously outdated, and Google has changed Qwiklabs into a "Pay to Create" gimmick that really doesn't work for anyone that is a Student, imo. $25+ to create a project, per project, and then 3 cents an hour while its running, can build up really fast. It was also changes and charges that Coursera was unaware of. So, how does this relate? I want to switch over to Microsoft IT Support courses and certification, but i want to make sure that its up to date, and that i won't run into another "out of the course" pay wall structure, because i really don't have that kind of money to throw around like that. It may not seem like a major deal to alot of people, but I am maintaining two households, on two continents, off one paycheck, and Im a kitchen employee...so Im sure alot of people can relate to how this is suddenly an issue. Can anyone verify how up-to-date that course is, from Microsoft, on Coursera? Im trying to change my life, and its already upsetting finding i wasted a weeks worth of effort.Set Up Plaud Note Pro with Microsoft Foundry
Prerequisites Riffado, up and running: follow the setup guide in the official Riffado repository to get it going with Docker Compose. A Microsoft Foundry (formerly Azure AI Foundry) resource, with the models you want deployed; in my case, whisper for transcription and o3-mini for summaries. A Plaud device, or any audio recordings you can import into Riffado. Once Riffado is up, head to the Settings page > Providers > Add Provider, and select Custom. This is where the Azure details will go. Why "OpenAI-compatible" isn’t one thing on Microsoft Foundry Azure AI Foundry exposes two different API surfaces on the same resource, and which one serves your model depends on the model: Surface Path shape Serves OpenAI-compatible? v1 route /openai/v1/… gpt-4o-transcribe, gpt-4o-mini-transcribe, chat models, embeddings Yes: Bearer auth, model in the body, no api-version needed Classic route /openai/deployments/{name}/… Whisper (and other legacy audio) No: deployment name lives in the URL, and ?api-version= is mandatory A generic OpenAI client (Riffado's included) can only speak the first dialect. It has nowhere to put a deployment name in the path and no way to append a query parameter. That single fact drives everything below. Part 1 - Transcription Whisper and the DeploymentNotFound mystery Symptom My very first transcription attempt in Riffado failed with 404 Resource not found. Off to a flying start. Configured provider: base URL https://<resource>.services.ai.azure.com, model whisper. Dead end #1: the missing path The first bug was mine: the base URL had no path. Riffado's OpenAI client appends /audio/transcriptions to whatever you give it, so requests were hitting https://<resource>…/audio/transcriptions, a path that doesn't exist on the resource at all. Fixing the base URL to end in /openai/v1 got us to a more interesting error: POST /openai/v1/audio/transcriptions · model=whisper {"error":{"code":"DeploymentNotFound","message":"The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again."}} Dead end #2: catalog ≠ deployment Worth checking before anything else: selecting a model in the Foundry catalog is not deploying it. GET /openai/v1/models lists everything you could deploy; only Deployments → Deploy model creates an endpoint that answers. If you get DeploymentNotFound, first confirm a deployment actually exists (the listing below requires only the API key): enumerate real deployments (classic control-plane, key auth) curl -s -H "api-key: $KEY" \ "https://<resource>.openai.azure.com/openai/deployments?api-version=2023-03-15-preview" # → {"data":[{"id":"whisper","model":"whisper","status":"succeeded",…}]} The actual cause Here is the part that nearly drove me mad: the deployment existed and was succeeded, yet the v1 route still said DeploymentNotFound. Because Whisper deployments are not served on the v1 route at all. They only answer on the classic path. Verified side by side with the same tiny WAV file: Request Result POST /openai/v1/audio/transcriptions · model=whisper · Bearer 404 DeploymentNotFound POST /openai/deployments/whisper/audio/transcriptions?api-version=2024-06-01 · Bearer 200 {"text":"you"} Same classic path, without ?api-version= 404 Resource not found Three constraints, then: Whisper needs the classic path; the classic path needs api-version; Riffado can send neither. One piece of good news hiding in the table: the classic route accepts Authorization: Bearer, not just Azure's api-key header, so the shim doesn't have to touch auth at all. The fix: a Caddy shim Drop a stock caddy:2-alpine container into the Compose network. Riffado points at it as if it were OpenAI; the shim rewrites the path, injects api-version, and proxies to Azure. The Bearer header passes through untouched. azure-shim.Caddyfile { admin off auto_https off } :80 { @transcribe path /v1/audio/transcriptions /audio/transcriptions handle @transcribe { rewrite * /openai/deployments/whisper/audio/transcriptions?api-version=2024-06-01 reverse_proxy https://<resource>.services.ai.azure.com { header_up Host <resource>.services.ai.azure.com } } handle { respond "azure-shim ok" 200 } } docker-compose.yml (added service) azure-shim: image: caddy:2-alpine restart: unless-stopped volumes: - ./azure-shim.Caddyfile:/etc/caddy/Caddyfile:ro Riffado's provider settings become: Field Value Base URL http://azure-shim/v1 Model whisper (must equal the deployment name) API key the Azure resource key (forwarded as Bearer) Verified From inside the Riffado container: POST http://azure-shim/v1/audio/transcriptions → 200 {"text":"…"}. Transcription works end-to-end in the UI. Part 2 · Summaries & titles o3-mini and the empty answer Symptom The summary button showed "An unexpected error occurred." The container logs were more honest: riffado-app logs Error generating title: TypeError: undefined is not an object (evaluating 'C.choices[0]') Riffado calls chat/completions and reads choices[0] without checking whether the response was an error. So anything the API refuses becomes "an unexpected error." What was it refusing? Cause 1: reasoning models reject the classic knobs o3-mini belongs to Azure/OpenAI's o-series reasoning models, which hard-reject parameters every classic chat client sends. Riffado sends temperature: 0.7 and max_tokens: 50 for titles (0.5 / 2000 for summaries), and o3-mini answers: POST /openai/v1/chat/completions · model=o3-mini HTTP 400 {"error":{"message":"Unsupported parameter: 'max_tokens' is not supported with this model. Use 'max_completion_tokens' instead.", …}} # and with max_tokens fixed: HTTP 400 {"error":{"message":"Unsupported parameter: 'temperature' is not supported with this model.", …}} Cause 2: reasoning tokens starve the output Stripping the bad params gets you to 200, and then comes a subtler failure, my personal favourite of this whole saga. Reasoning models spend completion tokens on internal "thinking" before emitting a single visible character. Riffado's 50-token title budget is consumed entirely by reasoning, and the reply comes back syntactically valid and empty: max_completion_tokens reasoning_effort finish_reason content 50 not set length "" (all 50 spent reasoning) 2000 not set stop "Q3 Budget Planning Strategy Meeting" 2000 low stop same, less reasoning overhead The fix: a Node shim that rewrites the request body Caddy can rewrite paths but not JSON bodies, so this shim is ~60 lines of dependency-free Node on node:20-alpine. Per request it: converts max_tokens → max_completion_tokens, strips temperature / top_p / penalties, floors the token budget at 4000, sets reasoning_effort: "low", maps /v1/* → /openai/v1/*, and forwards to the Azure resource. o3-shim.js const http = require('http'); const https = require('https'); const UPSTREAM_HOST = '<resource>.services.ai.azure.com'; // Params o-series reasoning models reject on chat/completions. const STRIP = ['temperature','top_p','presence_penalty', 'frequency_penalty','logprobs','top_logprobs']; const server = http.createServer((req, res) => { const chunks = []; req.on('data', c => chunks.push(c)); req.on('end', () => { let body = Buffer.concat(chunks); // Riffado's base_url is http://o3-shim/v1 → map to Azure's /openai/v1 let path = req.url; if (path.startsWith('/v1/')) path = '/openai' + path; const ct = (req.headers['content-type'] || '').toLowerCase(); if (ct.includes('application/json') && body.length) { try { const j = JSON.parse(body.toString('utf8')); if (j && typeof j === 'object' && !Array.isArray(j)) { if ('max_tokens' in j) { if (!('max_completion_tokens' in j)) j.max_completion_tokens = j.max_tokens; delete j.max_tokens; } // Reasoning spends tokens before any visible output; small // budgets (Riffado sends 50 for titles) return empty strings. if (Array.isArray(j.messages)) { j.max_completion_tokens = Math.max(Number(j.max_completion_tokens) || 0, 4000); if (!('reasoning_effort' in j)) j.reasoning_effort = 'low'; } for (const k of STRIP) delete j[k]; body = Buffer.from(JSON.stringify(j)); } } catch (_) { /* not JSON - forward untouched */ } } const headers = { ...req.headers, host: UPSTREAM_HOST, 'content-length': Buffer.byteLength(body) }; const up = https.request( { host: UPSTREAM_HOST, port: 443, method: req.method, path, headers }, upRes => { res.writeHead(upRes.statusCode, upRes.headers); upRes.pipe(res); } ); up.on('error', e => { res.writeHead(502, {'content-type':'application/json'}); res.end(JSON.stringify({error:{message:'o3-shim upstream error: '+e.message}})); }); up.end(body); }); }); server.listen(80, () => console.log('o3-shim listening on :80')); docker-compose.yml (added service) o3-shim: image: node:20-alpine restart: unless-stopped working_dir: /app command: ["node", "/app/o3-shim.js"] volumes: - ./o3-shim.js:/app/o3-shim.js:ro Add a second provider in Riffado (base URL http://o3-shim/v1, model o3-mini, the resource's API key) and set it as the default enhancement provider (summaries/titles), keeping the Whisper one as default for transcription. Riffado's exact title request (temperature: 0.7, max_tokens: 50) through the shim → 200, finish_reason: stop, real title text. A full meeting-transcript summary returns structured key points and action items. The final shape Reading it left to right: Riffado never talks to Azure directly. Transcription requests pass through azure-shim, a stock Caddy container that rewrites each request onto Whisper's classic deployment path and injects the mandatory api-version parameter. Summary and title requests pass through o3-shim, a tiny Node server that rewrites the request body into the shape o3-mini accepts and floors the token budget so the model's internal reasoning cannot starve the actual answer. As far as Riffado is concerned, it is simply talking to two ordinary OpenAI providers. Both shims live on the Compose network only; nothing is exposed publicly. Riffado is unmodified. Verification checklist Each layer, testable in isolation. Run these before blaming the app: smoke tests # 1. Key + resource alive? (v1 models listing, Bearer auth) curl -s -H "Authorization: Bearer $KEY" \ https://<resource>.services.ai.azure.com/openai/v1/models | head -c 200 # 2. Whisper answers on the classic path? curl -s -H "Authorization: Bearer $KEY" -F file=@test.wav \ "https://<resource>.services.ai.azure.com/openai/deployments/whisper/audio/transcriptions?api-version=2024-06-01" # 3. Shim translates correctly? (from inside the compose network) docker exec riffado-app node -e "fetch('http://azure-shim/') .then(r=>r.text()).then(console.log)" # 4. o3-mini via shim, sending the params Riffado sends? # (temperature + max_tokens:50; the shim must absorb both) If you'd rather not run shims Both shims exist because of the specific models chosen. Pick models that live natively on the v1 route and Riffado connects directly, with base URL https://<resource>.services.ai.azure.com/openai/v1 and zero extra containers: Transcription: deploy gpt-4o-mini-transcribe (or gpt-4o-transcribe) instead of Whisper. Summaries: deploy a non-reasoning chat model such as gpt-4o-mini, which happily accepts temperature and max_tokens. The shim approach earns its keep when you're standardized on specific models (Whisper's transcription quality, o3-mini's reasoning), or when you want a control point to add logging, retries, or budget caps later. For reference, this is what the finished setup looks like on Riffado's side. Each shim is registered as a plain Custom provider. Here is the whisper provider pointing at azure-shim, with Use for transcription ticked: And once both are saved, they sit side by side in the providers list, whisper tagged for transcription and o3-mini tagged for enhancement: A quick look at the Foundry portal In the Microsoft Foundry portal, head over to Models > AI Services and you will find a pleasant surprise: fifteen AI service models already deployed and ready to use, covering the Azure Speech family (including Voice Live and Speech to Text), Azure Translator, Azure Language, and Content Understanding: You can of course deploy another model for this, but the pre-deployed ones are a handy cost-saving option. Click on the Azure Speech – Voice Live radio button and you will be shown the Base URL and API Key, which you can then paste into the provider settings on Riffado's Settings page. A quick note on cost: these services are not free. They are billed pay-as-you-go based on usage. Azure Speech transcription is charged per audio hour, and Voice Live pricing is tiered by the model you choose. The free tier does include a monthly allowance, though. Check the Azure Speech pricing page before committing. And if you would rather deploy a dedicated transcription model such as whisper, Foundry gives you the flexibility to do just that. Open the model page in the catalogue, click Deploy, and go with Default settings unless you need custom quotas or guardrails: Let's test the setup On your Plaud device, just tap to start recording. The little LED bars light up to show it is listening: Or skip the device entirely and upload an audio file straight into Riffado using the Upload Audio button. Either way, the recording lands on the Recordings page; hit Transcribe and let the spinner do its thing: As you can see below, whisper, the transcription model we deployed earlier, even managed to transcribe a recording in Malay without a hitch. My 3:32 test clip came back as 186 words of clean Malay, with the language correctly detected and tagged: I have also set o3-mini as the enhancement provider, and it enhanced the transcription with a proper summary, key points, and title as well! The Meeting Notes-style summary came straight out of o3-mini through the shim, with zero manual prompting. Wrapping up What started as a TikTok-fuelled impulse buy nearly killed off by subscription pricing ended up as a fully self-hosted pipeline: Plaud for recording, Riffado as the interface, and Microsoft Foundry serving whisper and o3-mini behind two tiny shims. The total extra infrastructure came to two containers and roughly sixty lines of code, and not a single monthly subscription in sight. If you try this setup and run into a failure mode I have not covered here, do share it in the comments. Half the fun is in the debugging.
