Round Table: Building Browser-Capable Agents with the Browser Automation Tool
Some of the most valuable work still lives inside a browser: booking a class, pulling a figure off a dashboard, filling in a portal form, gathering research across a dozen tabs. These are exactly the tasks people wish an agent could just do. On 22 July 2026 at 2:30 PM BST (7:00 PM IST), the Microsoft Foundry community is running a 40‑minute Discord round table on the Browser Automation tool : how how it helps agents complete real browser workflows, where you see risk or friction, and what samples, docs, and product improvements would help you adopt it. This is a discussion, not a slideshow. Bring your real projects : the the web workflows you'd love to hand off, and the guardrails you'd want first. Join us in the Microsoft Foundry Discord community. Please arrive at the scheduled time for a quick tech check. Event at a glance What: Microsoft Foundry Discord Community Round Table : Building Browser-Capable Agents with the Browser Automation Tool When: 22 July 2026, 2:30 PM BST / 7:00 PM IST (40 minutes) Where: https://aka.ms/foundry/discord Event link https://discord.gg/Z8JZsrP5P5?event=1527676149264679013 Format: Interactive discussion : voice and chat, live polls, and a short prioritisation exercise voice and chat, live polls, and a short prioritisation exercise Who it's for: AI engineers and developers building agents that need to act on the web Opening question we'll start with: "What browser-based task would you love an AI agent to automate for you today?" The problem: the last mile of automation still runs in a browser Most real-world workflows eventually hit a website with no clean API , such as a supplier portal, an internal admin console, a booking page, or a legacy dashboard a supplier portal, an internal admin console, a booking page, a legacy dashboard. Traditional scripting can automate these, but selectors break, pages change, and every new site means another brittle script to maintain. What developers actually want is an agent that can look at a page, decide what to do, and do it : navigate, read, click, type, and hand back a structured result navigate, read, click, type, and hand back a structured result. That's the gap the Browser Automation tool in Microsoft Foundry is built to close , and doing it responsibly and doing it responsibly, with the right safeguards, is a big part of why we want your feedback. What is the Browser Automation tool? The Browser Automation Tool (BAT) gives Foundry agents the ability to drive a real browser to complete web workflows. It's available as an MCP tool, and it uses Playwright Workspaces , a generally available, cloud-scale service, a generally available, cloud-scale service : navigating, clicking at coordinates, typing, and applying filters as its headless browser infrastructure. When an agent gets a request, Foundry spins up an isolated, sandboxed browser session per interaction, so each run is private and segregated. How agents actually interact with a page BAT runs a perception–action loop. The model receives the current state of the page (including screenshots), decides the next action, and BAT executes it in the sandbox using Playwright and real oversight navigating, clicking at coordinates, typing, applying filters. After each action, BAT captures the updated state and sends it back to the model, repeating until the goal is met or the user stops. Because the model can parse HTML into a DOM, it can reason about the page rather than follow a fixed script. It also supports multi-turn conversations, so you can refine a request mid-flow to complete form-filling or scraping scenarios. Built for real use : watch the automation happen in real time for debugging. and real oversight Live View : a human-in-the-loop override for ambiguous or sensitive steps. watch the automation happen in real time for debugging. Take Control : each interaction gets its own sandboxed browser. a human-in-the-loop override for ambiguous or sensitive steps. Isolated sessions : for reliability, optimisation, and audit. each interaction gets its own sandboxed browser. Built-in observability : for internal systems (private preview). for reliability, optimisation, and audit. Private website browsing : Python, C#, JavaScript, Java, and the REST API. for internal systems (private preview). Broad SDK support , and the agent can make mistakes or be misled by malicious page content Python, C#, JavaScript, Java, and the REST API. A word on responsible use. BAT is powerful precisely because an AI can use credentials you share with it to reach email, financial, enterprise, or social accounts , watching for and the agent can make mistakes or be misled by malicious page content. You're responsible for reviewing your applications, scoping which credentials you provide, and adding your own mitigations. See the Foundry Agent Service transparency note. This is exactly the kind of trade-off we want to talk through together. Example scenario A user asks: "Report the year-to-date percent change of Microsoft's stock price." The agent navigates to a finance site, enters MSFT in the search bar, opens the stock page, clicks the YTD view on the chart, reads the value, and returns a clean, structured answer ; that's the no bespoke scraper, no hard-coded selectors, and a full trace of what it did. Discussion prompt: "Where would browser automation fit into your current projects or workflows?" How setup works (the short version) You'll want to understand the wiring before you scale, so it's worth a look ahead of the session. There are two moving parts: Create a Playwright Workspace in the Azure portal, enable the access token auth method, and grab the wss:// browser endpoint. Give your project identity a Contributor (or custom) role on the workspace. Connect the tool in Foundry under Build > Tools: create a toolbox, add Browser Automation, point it at your Playwright workspace and auth type, and publish. Copy the Project connection ID from the tool's details page : how agents interact with the web, example use cases, and responsible use. that's the BROWSER_CONNECTION_ID in your code. What we'll cover in the 40 minutes Welcome & opening question (0:00–0:03) : navigate, gather, interact, and return a structured result, end to end. the browser task you'd most love to automate. What is Browser Automation (0:03–0:07) : the workflows you're building, public vs. internal targets, and where you'd pick automation over scripting. how agents interact with the web, example use cases, and responsible use. Scenario walkthrough (0:07–0:12) : what agents may do autonomously, what needs approval, and the observability and enterprise safeguards you'd require. navigate, gather, interact, return a structured result : your biggest adoption blockers, missing docs, and the SDK samples and demos you'd prioritise. end to end. Use cases & opportunities (0:12–0:22) : vote live on top use cases, challenges, and feature requests. the workflows you're building, public vs. internal targets, and where you'd pick automation over scripting. Trust, security & governance (0:22–0:31) , and which would benefit most from a capable agent. what agents may do autonomously, what needs approval, and the observability and enterprise safeguards you'd require. Developer experience feedback (0:31–0:36) ; which should always require approval. your biggest adoption blockers, missing docs, and the SDK samples and demos you'd prioritise. Prioritisation & next steps (0:36–0:40) , sometimes with credentials, vote live on top use cases, challenges, and feature requests. Come prepared to talk about The browser-based workflows you're building today : navigation, data gathering, form filling, and research, via an MCP tool powered by Playwright Workspaces. and which would benefit most from a capable agent. Whether your scenarios target public websites, internal systems, or both. Why you'd choose browser automation over traditional scripting. Which actions you'd let an agent perform autonomously : a perception-action loop with screenshots and DOM parsing handles pages that break brittle scripts. and which should always require approval. The observability, audit, and enterprise safeguards you'd expect before running this in production. The examples, samples, and tutorials that would help you get started fastest. Responsible and secure by design Because BAT lets an agent take real actions on live websites : isolated sessions, Live View, Take Control, and observability for reliability and audit. sometimes with credentials : scope credentials carefully and add your own mitigations; the tool is powerful and in preview. governance is a first-class part of the conversation, not a footnote. Isolated per-session sandboxes, Live View, Take Control human-in-the-loop, and built-in observability are there so you can see, pause, and audit what an agent does. Bring your trust concerns, required guardrails, and governance requirements: they directly shape the roadmap. Note: the Browser Automation tool is in preview; APIs and capabilities may change, and it isn't recommended for production workloads yet. Key takeaways Browser Automation lets Foundry agents complete real web workflows : this round table feeds directly into the engineering and product teams. navigation, data gathering, form filling, research , and arrive on time for the tech check. via an MCP tool powered by Playwright Workspaces. Agents reason, not just replay , and browser-capable agents are how we cross it. a perception–action loop with screenshots and DOM parsing handles pages that break brittle scripts. Oversight is built in: isolated sessions, Live View, Take Control, and observability for reliability and audit. Responsibility is shared: scope credentials carefully and add your own mitigations; the tool is powerful and in preview. Your feedback shapes the product: this round table feeds directly into the engineering and product teams. Save your spot Add it to your calendar: 22 July 2026, 2:30 PM BST / 7:00 PM IST, and arrive on time for the tech check. Join the community: https://aka.ms/foundry/discord Prep with the sample: explore the browser automation sample in foundry-samples. Read the docs: Automate browser tasks with Foundry agents and the hosted-agent quickstart. Event registration link https://discord.gg/Z8JZsrP5P5?event=1527676149264679013 The last mile of automation still runs in a browser, and browser-capable agents are how we cross it. Come tell us what you'd automate, where you'd draw the line, and what you'd need to trust it in production. See you on 22 July.Building AI Agents from Zero to Production
Building AI Agents from Zero to Production Most agent demos stop at "it answered my question." Production doesn't. The gap between a notebook that calls an LLM and a governed, observable, multi-agent system your organisation can actually depend on is where real engineering happens, evaluation, deployment, data sovereignty, tool governance, and cross-team interoperability. Microsoft's open-source course Building AI Agents from Zero to Production walks that entire arc in seven lessons, using one realistic use case and the Microsoft Agent Framework (MAF) plus Microsoft Foundry. This post is a developer-focused tour of what it teaches, the architecture decisions behind each stage, and the code patterns that matter when you move from prototype to production. Who this is for AI engineers building their first or first production, agent system. Backend and full-stack developers integrating agents into real applications and CI/CD. Cloud architects who need data sovereignty, private networking, and governance around agent workloads. Technical leads deciding how to standardise tools and orchestration across multiple teams. The samples are Python 3.12+, served through Microsoft Foundry using GPT-5 series models (for example gpt-5.1 ). Lesson 4 adds a TypeScript/React frontend. You will want an Azure subscription and the Azure CLI. The AI Agent Development Lifecycle The course is organised around a lifecycle rather than a feature list. Each lesson is a stage, and each stage assumes the previous one is solved: # Stage The production question it answers 1 Agent Design What should each agent do, and how do they hand off? 2 Agent Development How do I build and run them with the Agent Framework? 3 Agent Evaluations How do I know they actually work — and keep working? 4 Agent Deployment How do I ship one as a hosted service with a UI and CI gate? 5 Production Hosted Agents How do I meet enterprise data, network, and governance needs? 6 Microsoft Toolbox How do I govern tools once, and reuse them across teams? 7 Multi-Agent & A2A How do agents from different teams interoperate safely? The thread running through all seven is a single scenario: a Developer Onboarding agent system that helps a new hire find the right teammates, get a sensible first task, and pull learning resources and code snippets. It is deliberately mundane, which is exactly why it exposes the production concerns that flashy demos hide. Lesson 1 — Agent Design: three components, one graph The course defines an agent by three parts: an LLM for reasoning, tools to act, and memory to retain context. The design work is context engineering — making sure the right information reaches the model at the right moment, no more and no less. Rather than one monolithic assistant, the onboarding system is split into specialists coordinated by a triage agent using handoff orchestration: Agent Job Tool Employee Search Answer org and people questions Foundry file search over an employee-directory vector store Task Recommendation Suggest 1–3 GitHub issues for the new dev GitHub MCP Server (reads recent commits + open issues) Code Assistant Provide resources and runnable snippets Microsoft Learn MCP + Code Interpreter Architecturally this is a directed graph: User → Triage → [Employee, Learning, Coding] . Splitting responsibilities early pays off later, each agent gets a tightly scoped prompt (less hallucination), can be evaluated independently, and can be upgraded without touching its peers. Lesson 2 — Development: standalone agents with MAF Here the design becomes code. Each specialist is a small, independently runnable service built with the Microsoft Agent Framework, authenticated to Foundry with your Azure CLI login. Setup is deliberately boring: az login az account set --subscription "<your-subscription-id>" cp .env.example .env # Fill FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL (e.g. gpt-5.1) # Create the employee-directory vector store once; note the printed VECTOR_STORE_ID python lesson-2-agent-development/setup_vector_store.py # Start an agent — serves on http://localhost:8090 python lesson-2-agent-development/employee-search-agent.py The FoundryChatClient auto-reads any FOUNDRY_ -prefixed environment variables and uses AzureCliCredential , so there are no keys in code. The lesson ships six samples, each on its own port, so you can chat with them individually in the local DevUI before wiring them together: Sample Tool Port employee-search-agent.py Foundry file search / vector store 8090 task-recommendation-agent.py GitHub MCP Server 8095 azure-learning-agent.py Microsoft Learn MCP 8092 coding-agent.py Code Interpreter 8093 learning-recommendation-agent.py Learn MCP + reasoning 8091 agent-orchestration.py Multi-agent handoff 8094 Why this matters: keeping each agent as its own process with its own port is a testability decision, not an accident. You can smoke-test one specialist in isolation, then compose them in agent-orchestration.py . Lesson 3 — Evaluation: you can't unit-test a probability distribution This is the lesson that separates a demo from a product. Agents are non-deterministic, so traditional assertions don't fit. The course uses three complementary layers: Observability / tracing — always on, via OpenTelemetry to Application Insights. Smoke tests — fast, run on every deploy. Evaluations — deeper, model-based scoring run on-demand or nightly. Turning on tracing is a single call: from agent_framework.foundry import FoundryChatClient client = FoundryChatClient() client.configure_azure_monitor() # export traces + metrics to Application Insights For quality it uses Foundry's built-in "LLM-as-a-judge" evaluators against real persisted responses (identified by response_id ), not freshly regenerated ones: Evaluator evaluator_name Measures Relevance builtin.relevance Does the response address the request? Groundedness builtin.groundedness Is it supported by retrieved data (no hallucination)? Tool-call accuracy builtin.tool_call_accuracy Were the right tools called with the right arguments? Tool-output utilization builtin.tool_output_utilization Did the agent actually use tool results? The judge model is set independently via AZURE_AI_MODEL_DEPLOYMENT_NAME , so you can evaluate a cheap production model with a stronger one. The run prints a report_url that deep-links into the Foundry portal. Lesson 4 — Deployment: a hosted agent, a UI, and a CI gate Now the agent becomes a managed service. It is deployed as a Foundry Hosted Agent a Microsoft-managed execution environment and fronted by an OpenAI ChatKit React UI talking to a FastAPI backend: ChatKit React (3000) → FastAPI backend (8001) → Foundry Hosted Agent → tools Building the agent is declarative attach tools, name it, serve it: agent = client.as_agent( name="DevOnboardingAgent", instructions="...", tools=[file_search_tool, learn_mcp_tool], ) # served with: from_agent_framework(agent).run() The recommended deploy path is the Azure Developer CLI: cd hosted-agent azd auth login azd agent deploy The genuinely production-minded part is the smoke test as a post-deploy CI gate. Six cases cover reachability, each scenario, off-topic prompt adherence, and multi-turn threading (verifying state via previous_response_id ). The GitHub Action runs them against the freshly deployed agent: export FOUNDRY_TOKEN=$(az account get-access-token \ --resource https://ai.azure.com/ --query accessToken -o tsv) python runner.py \ --project-endpoint "https://<account>.services.ai.azure.com/api/projects/<project>" \ --agent-name dev-onboarding \ --tests-file tests/smoke-tests.json Pitfall to remember: the token audience must be https://ai.azure.com/ . A cognitiveservices.azure.com token is rejected by the Responses API — a mistake that costs many engineers an afternoon. Lesson 5 — Production: separating where an agent runs from where its data lives The pivotal concept for enterprise readiness is the distinction between a Hosted Agent (compute, scaling, identity) and a Capability Host (where conversation history, files, and embeddings actually reside): Concern Hosted Agent Capability Host Compute / scaling / identity ✅ Provided — Conversation history Microsoft-managed default Redirect to your Azure Cosmos DB File uploads Microsoft-managed default Redirect to your Azure Storage Vector embeddings Microsoft-managed default Redirect to your Azure AI Search Required to run the agent? ✅ Yes ❌ Optional Required for data sovereignty? ❌ Not sufficient ✅ Yes "Basic" setup uses Microsoft-managed storage and is perfect for getting started. "Standard" setup redirects each data plane to your own Azure resources through a project-level capability host, this is how you keep customer data in your tenant, inside your network boundary: PUT .../accounts/{account}/projects/{project}/capabilityHosts/{name}?api-version=2025-06-01 { "properties": { "capabilityHostKind": "Agents", "threadStorageConnections": ["my-cosmosdb-connection"], "vectorStoreConnections": ["my-ai-search-connection"], "storageConnections": ["my-storage-connection"] } } Operational constraints worth internalising before you provision: there is one capability host per scope (a second attempt returns 409 Conflict ), configuration is immutable (delete and recreate to change it), deletion is destructive, and the account-level host must exist before the project-level one. Lesson 6 — Toolbox: govern tools once, reuse everywhere Left unchecked, every team re-implements the same tools, scatters credentials, and loses governance visibility. The Microsoft Foundry Toolbox solves this by exposing a curated, versioned set of tools behind a single MCP-compatible endpoint, with credentials held in Foundry connections rather than agent code. You build a toolbox version once: from azure.ai.projects.models import MCPTool, ToolboxSearchPreviewTool, WebSearchTool toolbox_version = project.toolboxes.create_toolbox_version( name="agent-tools", description="Web search + an MCP server + tool search", tools=[ WebSearchTool(), MCPTool( server_label="myserver", server_url="https://your-mcp-server.example.com", require_approval="never", project_connection_id="my-key-auth-connection", # credentials live in Foundry ), ToolboxSearchPreviewTool(), ], ) And every agent consumes it through one endpoint, no per-team tool code: from agent_framework import MCPStreamableHTTPTool mcp_tool = MCPStreamableHTTPTool( name="toolbox", url=TOOLBOX_ENDPOINT, # {project_endpoint}/toolboxes/{name}/mcp?api-version=v1 http_client=http_client, load_prompts=False, ) agent = chat_client.as_agent(name="my-toolbox-agent", instructions="...", tools=[mcp_tool]) Versioning is blue/green: create a new version, test it on its version-specific endpoint, then promote it to default and every consumer picks it up with zero code changes. A Guardrail (RAI) policy can be applied at the toolbox layer, independent of model-level content filters. Note the toolbox management APIs are currently preview; the portal or VS Code Foundry Toolkit are practical alternatives for creation today. Lesson 7 — Multi-Agent & A2A: agents as networked peers The final lesson contrasts two ways agents collaborate: Handoff / Workflow — in-process, same codebase, fastest, tightest coupling. Agent-to-Agent (A2A) — cross-process over an open protocol, so agents from different teams, orgs, or frameworks interoperate. A2A gives each agent a discoverable Agent Card at /.well-known/agent-card.json and a task lifecycle (submitted → working → completed/failed). The elegant part: A2AExecutor wraps an existing MAF agent with no changes to that agent's code. from agent_framework.a2a import A2AExecutor from a2a.server.apps import A2AStarletteApplication from a2a.server.tasks import InMemoryTaskStore agent_card = AgentCard( name="Coding Assistant", url="http://localhost:9000/", version="1.0.0", capabilities=AgentCapabilities(streaming=True), skills=[AgentSkill(id="generate-code", name="Generate code", tags=["code"])], ) request_handler = DefaultRequestHandler( agent_executor=A2AExecutor(agent), # wraps your existing MAF agent unchanged task_store=InMemoryTaskStore(), ) app = A2AStarletteApplication(agent_card=agent_card, http_handler=request_handler).build() Consuming a remote agent then looks exactly like calling a local one: from agent_framework.a2a import A2AAgent remote_agent = A2AAgent(name="remote-coding-assistant", url="http://localhost:9000") result = await remote_agent.run("Write a Python function that reverses a string.") Because an A2AAgent can be a participant inside a HandoffBuilder workflow, you can mix in-process routing with remote services in the same orchestration. For enterprise use, A2AAgent accepts an auth_interceptor for bearer tokens, and the Agent Card carries security_schemes . Responsible and secure by design Production readiness in this course is not just uptime, it is governance: Identity over keys — AzureCliCredential and managed identity throughout; no secrets in code. Least privilege — CI runners get a scoped Azure AI User role assignment on the specific project. Data sovereignty — capability hosts keep conversation history, files, and embeddings in your own Cosmos DB, Storage, and AI Search. Tool approval and guardrails — MCP approval_mode and toolbox-level RAI policy gate what agents can do. Grounded evaluation — groundedness and tool-utilization scoring catch hallucination and unused-tool behaviour before users do. Cost hygiene — the lessons create real Azure resources; delete the resource group when done: az group delete --name <rg> --yes --no-wait . Key takeaways Design as a graph of specialists. Handoff orchestration with tightly scoped agents beats one monolith on reliability and testability. One .run() contract, many backends. The Agent Framework keeps orchestration code stable from local dev to hosted production. Evaluate continuously. Tracing + smoke tests + model-based evaluators are three layers, not alternatives. Separate compute from data. Hosted Agents run the agent; Capability Hosts give you sovereignty — you need both for enterprise. Govern tools centrally. A versioned toolbox behind one MCP endpoint kills tool sprawl and credential duplication. Open protocols for interop. A2A lets agents cross team, org, and framework boundaries without rewrites. Get started Clone the repo (skip the 50+ translations for a faster download) and work through the lessons in order: git clone --filter=blob:none --sparse https://github.com/microsoft/Building-AI-Agents-From-Zero-To-Production.git cd Building-AI-Agents-From-Zero-To-Production git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' References Building AI Agents from Zero to Production — course repo Microsoft Agent Framework Microsoft Foundry documentation Agent-to-Agent (A2A) protocol specification a2a-python SDK AI Agents for Beginners MCP for Beginners Microsoft Foundry DiscordAzure Function App — Queue-Based Architecture for Long-Running Sync Jobs
The Problem: HTTP Triggers and Long-Running Jobs Don't Mix Here's a situation you've probably run into: you have a job that needs to loop over dozens of Azure resources, call APIs, and do real work. You wrap it in an HTTP-triggered Azure Function so it can be called on demand. It works great and after a few minutes, the caller gets a 504 Gateway Timeout. The 230-second limit is enforced by Azure Front Door / the platform load balancer. It cannot be overridden by app settings or host configuration. Any HTTP trigger that runs longer than ~3.5 minutes will timeout for the caller. In our case, the job iterates over 30+ Azure subscriptions — for each one it switches context, lists resources, and triggers image imports. Total runtime: anywhere from 2 to 10 minutes depending on how many ACRs need updating. Way over the limit. The Solution: Decouple Request from Execution via a Queue The fix is clean once you see it: the HTTP trigger shouldn't do the work — it should just accept the work and hand it off. That's what a queue is for. The flow splits into two independent phases: Request phase — The HTTP trigger validates the caller (JWT + app role check), packages the job parameters into a queue message, and returns 202 Accepted. This takes under 3 seconds. Execution phase — A Queue Trigger picks up the message and runs the actual sync. No HTTP connection involved, so there's no timeout. On a Dedicated (P-series) plan, execution time is unlimited. Approach What the caller gets Result HTTP trigger → run sync inline Waits for the full job to complete 504 TIMEOUT after 230 seconds HTTP trigger → Queue → Queue Trigger 202 Accepted immediately NO TIMEOUT job runs as long as needed 🤸♀️There's an added bonus - Reliability in Azure Queue Storage: Azure Storage Queues give you automatic retry out of the box. If the job crashes halfway through, the message becomes visible again after a visibility timeout and the Queue Trigger picks it up for a retry — up to 5 attempts before the message is moved to the poison queue. No retry logic to write 🤸♀️. Locking Down the Endpoint Since the HTTP trigger is the public entry point, it needs solid auth. We layer two things: ⭐Use EasyAuth for the "is this a real Entra ID token?" check, and a custom App Role for the "is this person allowed to trigger syncs?" check. These are independent concerns and should stay that way. Layer What it does How EasyAuth (Entra ID) Rejects requests without a valid Entra ID Bearer token — before your code even runs Configured at the Function App level via the Authentication blade App Role check Validates that the token contains the SyncJob.Execute role — only assigned users/SPs can trigger the job Decoded in the function code from the JWT roles claim Managed Identity Authenticates the Function App to Azure APIs (no credentials in code) Connect-AzAccount -Identity — identity assigned via RBAC One gotcha worth knowing: when using v2 tokens (which is the default with modern App Registrations), the aud claim in the token is the raw App ID GUID — not the api:// prefixed URI. You need to explicitly add both forms to your allowedAudiences in EasyAuth, otherwise valid tokens get rejected. APP_ID="<your-app-id>" TENANT_ID="<your-tenant-id>" FUNCTION_APP_URL="https://<your-function-app>.azurewebsites.net" # Interactive login (device code flow — works from any terminal) az login --tenant "${TENANT_ID}" \ --scope "api://${APP_ID}/.default" \ --use-device-code TOKEN=$(az account get-access-token \ --scope "api://${APP_ID}/.default" \ --query accessToken -o tsv) # Trigger the sync — returns 202 immediately curl -s -X POST "${FUNCTION_APP_URL}/api/SyncContainerRegistryHttpTrigger" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" Passing Parameters Through the Queue One nice property of this pattern: the queue message is just JSON, so you can pass whatever parameters the job needs. In our case, we pass a subscriptionFilter wildcard so callers can target a subset of subscriptions without touching any code. The parameter travels the full chain: HTTP body → queue message → Queue Trigger → PowerShell script parameter. Here's how each step handles it. Step 1 — HTTP Trigger reads the body and enqueues the message using the Push-OutputBinding output binding. Azure Functions wires the binding to the queue automatically — no SDK call needed: param($Request, $TriggerMetadata) # ... decode the JWT, check role assignment $queuePayload = @{ triggeredBy = $decoded.Payload.upn ?? $decoded.Payload.oid triggeredAt = (Get-Date -Format 'o') subscriptionFilter = if ($body.subscriptionFilter) { $body.subscriptionFilter } else { "*" } } | ConvertTo-Json -Compress Push-OutputBinding -Name QueueMessage -Value $queuePayload Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{ StatusCode = [System.Net.HttpStatusCode]::Accepted Body = @{ message = "Sync job queued. Check Azure Monitor logs for execution status." } }) ⭐Push-OutputBinding is how Azure Functions PowerShell workers write to output bindings (queues, blobs, HTTP responses…). The binding name QueueMessage maps to the queue defined in function.json — the runtime handles serialisation and delivery. Step 2 — Queue Trigger passes the filter to the script as a named parameter: param($QueueItem, $TriggerMetadata) Write-Host "Triggered SyncContainerRegistry via Storage Queue. Payload: $QueueItem" $subscriptionFilter = if ($QueueItem.subscriptionFilter) { $QueueItem.subscriptionFilter } else { "*" } $SubscriptionFilter = $subscriptionFilter . "$PSScriptRoot/../SyncContainerRegistry/run.ps1" Step 3 — Long running job with the filter as parameter: param($Timer) if (-not $SubscriptionFilter) { $SubscriptionFilter = "*" } $subscriptions = Get-AzSubscription | Where-Object { $_.Name -like $SubscriptionFilter } foreach ($subscription in $subscriptions) { Set-AzContext -SubscriptionId $subscription.Id | Out-Null # ... do the work } Targeting a subset of subscriptions # Sync all subscriptions (default — omit the body) curl -s -X POST "${FUNCTION_APP_URL}/api/SyncContainerRegistryHttpTrigger" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" # Sync only subscriptions matching a pattern curl -s -X POST "${FUNCTION_APP_URL}/api/SyncContainerRegistryHttpTrigger" \ -H "Authorization: Bearer ${TOKEN}" \ -H "Content-Type: application/json" \ -d '{"subscriptionFilter": "*project-alpha*"}' ⭐PowerShell's -like operator uses * as a wildcard anywhere in the string. The pattern *project-alpha* matches sub-mycompany-project-alpha-prd, sub-mycompany-project-alpha-dev, etc. A pattern without a leading * only matches from the start of the string — keep this in mind when naming subscriptions. Pushing a Message Directly via PowerShell You can also push a message straight to the queue without going through the HTTP trigger — useful for testing, scripting, or bypassing the auth layer in a controlled environment. Connect-AzAccount # or -Identity for a Managed Identity context $storageAccount = "<your-storage-account>" $queueName = "sync-job-queue" # Build the payload — same shape the HTTP trigger produces $payload = @{ triggeredBy = $env:USERNAME triggeredAt = (Get-Date -Format 'o') subscriptionFilter = "*project-alpha*" # or "*" for all } | ConvertTo-Json -Compress # Get a queue client via the connected account (no key needed) $ctx = New-AzStorageContext -StorageAccountName $storageAccount -UseConnectedAccount $queue = Get-AzStorageQueue -Name $queueName -Context $ctx $queue.QueueClient.SendMessage($payload) ⭐ -UseConnectedAccount authenticates via the current Connect-AzAccount session — no storage key required, as long as your identity has the Storage Queue Data Message Sender role on the storage account. The Queue Message The HTTP trigger packages the caller identity and filter into a simple JSON payload before enqueuing. The Queue Trigger reads it back as a deserialised PowerShell object — no manual JSON parsing needed. { "triggeredBy": "user@company.com", "triggeredAt": "2026-06-01T11:03:55.570+02:00", "subscriptionFilter": "*project-alpha*" } Design Decisions at a Glance Decision Choice Why Async execution Azure Storage Queue HTTP trigger has a hard 230s timeout. The sync job takes 2–10 minutes. The queue decouples acceptance from execution — and gives us retry for free. Authentication EasyAuth + App Role No credentials in code. Access is controlled via Entra ID app roles — revocable per user without touching infrastructure. Azure identity Managed Identity No secrets to rotate or store. The Function App authenticates to Azure APIs using its platform-assigned identity. Job parameter Wildcard filter via queue payload Lets callers target any subscription subset without code changes. The filter travels through the queue — the Queue Trigger just passes it along. Hosting plan Dedicated (P-series) Consumption plan caps function execution at 10 minutes. A Dedicated plan has no execution time limit — essential when the job can run longer. See you in the Cloud JamesdldToken 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 DocsGitHub Copilot App - Canvas Is Not a UI Builder
What if your development environment didn't just help you write code, but helped you observe, steer, and evolve a living system while it runs? That's the shift GitHub Copilot App Canvas represents. Canvas redefines how developers interact with agent-driven software: not by building traditional user interfaces, but by creating interactive environments where humans and AI co-create, test, and iterate in real time. This post walks through a real Canvas extension we built, a Multi-Agent Dev Canvas that demonstrates how Canvas becomes a runtime observability and control plane for an agent-driven system. We'll cover why Canvas exists, how it differs from traditional UI development, and how you can use it to accelerate the design-test-evolve loop for any multi-agent application. The Misconception: "Canvas Is for Building UIs" The first instinct many developers have when they see Canvas is to treat it like a UI framework, a place to build dashboards, boards, or user-facing applications. That's not what Canvas is for. Here's the distinction that matters: Traditional UIs are for using software. They serve end-users who interact with a finished product. Canvas is for shaping software while it runs. It serves developers and AI agents who are actively building, testing, and evolving a system. Canvas solves problems your final UI should never try to solve in a visible way. It's the observability layer, the control plane, the validation surface — all the things you need during development that disappear before production. Think of it this way: you wouldn't ship your debugger to users, but you absolutely need it while building. What We Built: A Multi-Agent Dev Canvas To demonstrate Canvas as a development runtime, we built a Multi-Agent Dev Canvas, a standalone GitHub Copilot Canvas extension (this repo, copilot-canvas-runtime) that treats an entire multi-agent system as a living, observable environment. The same pattern applies to any agent-driven system built on services such as Microsoft Foundry. The Multi-Agent Dev Canvas: a runtime observability and control plane where developers and AI agents collaborate to design, test, and evolve an agent-driven system in real time. The canvas provides four integrated panels: System View: See Your Agents Working Five specialised agents are displayed as live cards with real-time status indicators. Each card shows the agent's name, responsibility, current status (idle, running, done, or error), task count, and last action taken. When an agent is active, its card pulses blue. When it fails, it glows red. You see the system breathe. decompose_system — Breaks requirements into agent tasks execute_workflow — Coordinates agents to perform tasks validate_output — Runs evaluation tests and returns structured results update_system_design — Modifies architecture based on feedback track_state — Persists and updates system state over time Task Flows: Watch Work Move Through the Pipeline Below the agents, a flow graph visualises how tasks route between agents. When you decompose a system requirement like "Build an AI-powered code review agent," the canvas shows five components (pr-ingestion, code-analysis, feedback-generator, learning-loop, notification-service) flowing from the decomposer to the executor and designer agents. Each flow carries a status badge, pending, pass, or fail. Validation Panel: Continuous Testing, Not Afterthought Testing The validation panel displays structured test results with pass/fail badges and reasoning. When you run validation, each test case evaluates against specific criteria: ✅ "PR ingestion handles large diffs" — Meets criteria: process diffs over 5,000 lines without timeout ❌ "Feedback is actionable" — Failed: does not satisfy criteria that each suggestion includes a code fix ✅ "Learning loop converges" — Meets criteria: accept rate improves over 10 iterations ✅ "Notifications are non-blocking" — Meets criteria: delivery latency under 500ms This isn't a test runner you invoke separately, it's a validation surface embedded in the development loop. You see failures the moment they happen, in context, alongside the agents and flows that produced them. Live State Timeline: Every Mutation, Visible The right panel tracks every state change with timestamps. Decomposition events, workflow executions, validation runs, failure injections — all appear chronologically. This is the system's memory, visible to both the human developer and the AI agents working alongside them. Canvas as a Runtime: The Key Capabilities What makes Canvas a runtime rather than a display layer is that the agent can act through it. The canvas exposes seven agent-callable actions: Action What It Does decompose_system Accept requirements and components, generate task flows, update the system design execute_workflow Run pending tasks through the agent pipeline, produce artifacts validate_output Evaluate test cases against criteria, return structured pass/fail with reasoning update_system_design Modify the architecture description, constraints, or component list live track_state Read the full system state — agents, flows, validations, history, artifacts inject_failure Force an agent into an error state to test system adaptation pause_resume Toggle execution on and off The human developer can click Decompose, Execute, or Validate directly in the canvas. The AI agent can invoke the same actions programmatically. Both parties operate on the same surface, the same state, the same system, that's what makes Canvas collaborative in a way traditional tooling is not. Why This Matters: Canvas vs. Figma vs. Traditional UIs It helps to position Canvas against tools developers already know: Figma is Human-to-Human collaboration on design. Multiple people interact with the same visual surface, but nothing executes. It's a design tool. Traditional UIs are Human-to-System. Users interact with finished software through a polished interface. Canvas is Human-to-AI-to-System. It's a shared space where things actually execute. The developer steers, the AI acts, and the system evolves, all visible, all in real time. Canvas is collaborative in the Figma sense — it's a shared space, it's visual, multiple participants interact with the same surface. But unlike Figma, the participants include AI agents, and the surface isn't a mockup — it's a live system. How the Extension Works: Under the Hood A Canvas extension is a standard GitHub Copilot CLI extension, a single extension.mjs file that speaks JSON-RPC over stdio. The key components: 1. State Management Each canvas instance maintains its own system state: agents, task flows, validations, a state history timeline, artifacts, and the current system design. State is held in-memory per instance and pushed to the iframe via Server-Sent Events whenever it changes. function createInitialState() { return { agents: [ { id: "decomposer", name: "decompose_system", status: "idle", responsibility: "Break requirements into agent tasks" }, { id: "executor", name: "execute_workflow", status: "idle", responsibility: "Coordinate agents to perform tasks" }, // ... three more agents ], taskFlows: [], validations: [], stateHistory: [], artifacts: [], systemDesign: { description: "", constraints: [], components: [] }, execution: { paused: false, stepCount: 0 }, }; } 2. Real-Time Updates via Server-Sent Events The canvas runs a loopback HTTP server per instance. The iframe connects to an /events endpoint and receives state updates as they happen — no polling, no websocket complexity. if (req.url === "/events") { res.writeHead(200, { "Content-Type": "text/event-stream", "Cache-Control": "no-cache" }); clients.add(res); // Push current state immediately on connect res.write(`data: ${JSON.stringify(getState(instanceId))}\n\n`); } 3. Dual Interaction Model Every action is available through two paths. The human clicks a button in the iframe, which POSTs to the local server. The AI agent calls invoke_canvas_action through the SDK. Both paths mutate the same state and trigger the same SSE broadcast. Neither is privileged over the other. 4. Canvas Declaration The canvas registers with the Copilot SDK using createCanvas , declaring its identity, description, and all agent-callable actions with JSON Schema validation on inputs: createCanvas({ id: "multi-agent-dev", displayName: "Multi-Agent Dev Canvas", description: "Runtime observability and control plane for multi-agent development", actions: [ { name: "decompose_system", description: "Break requirements into agent tasks", inputSchema: { type: "object", properties: { requirements: { type: "string" }, components: { type: "array", items: { type: "string" } } }, required: ["requirements"] }, handler: async (ctx) => { /* ... */ }, }, // ... six more actions ], open: async (ctx) => { /* start server, return URL */ }, onClose: async (ctx) => { /* clean up */ }, }); Scenarios This Enables The Multi-Agent Dev Canvas supports four development scenarios that would be impossible with traditional tooling: 1. End-to-End Feature Design Tell the agent "Build an AI-powered code review system." Watch it decompose the requirement into five components, route tasks to specialist agents, execute the workflow, and validate the outputs, all visible in real time. Iterate by modifying constraints or components and re-running. 2. Live Agent Collaboration Observation See how agents hand off work to each other. The flow graph shows which agent produced what, which tasks are pending, and where bottlenecks form. This is the kind of observability you need when debugging multi-agent orchestration but would never expose in a production UI. 3. Fault Injection and Adaptation Testing Use inject_failure to force an agent into an error state. Watch how the system responds. Does the orchestrator recover? Do downstream tasks fail gracefully? This chaos-engineering approach, applied during development, visible in real time, catches integration failures before they reach production. 4. Validation-Driven Iteration Define test criteria, run validation, see which tests fail, update the system design, re-run. The validation panel isn't a separate CI pipeline, it's embedded in the development surface, creating a continuous feedback loop between design decisions and their measurable outcomes. Getting Started: Build Your Own Canvas Extension To create a Canvas extension in your own project: Read the SDK docs — Run extensions_manage({ operation: "guide" }) in GitHub Copilot CLI to get the canonical documentation paths. Scaffold — Run extensions_manage({ operation: "scaffold", kind: "canvas", name: "my-canvas", location: "project" }) to generate the boilerplate. Implement — Edit extension.mjs with your canvas logic: state model, actions, renderer HTML, and SSE updates. Reload — Run extensions_reload to activate your changes. Drive — Open with open_canvas , invoke actions with invoke_canvas_action , and iterate. The canvas extension lives in .github/extensions/your-canvas/extension.mjs for project-scoped extensions, or in your user extensions directory for personal use. No package.json needed, the github/copilot-sdk import is auto-resolved. Key Takeaways Canvas is a development runtime, not a UI framework. You don't build Canvas instead of your UI, you use Canvas to figure out, test, and evolve the UI and system before and during building it. Canvas solves problems your final UI should never expose. Agent observability, fault injection, live state mutation, validation feedback loops, these are development concerns, not user concerns. Canvas is Human-to-AI-to-System collaboration. Both the developer and the AI agent operate on the same surface, the same state, the same running system. It's Figma-like collaboration, but with AI agents, and things actually execute. Canvas turns debugging, testing, and execution into a continuous visual feedback loop. Instead of switching between an editor, a terminal, a test runner, and a monitoring dashboard, you have one surface where the system lives and evolves. Canvas extensions are lightweight. A single extension.mjs file, no dependencies, loopback HTTP server with SSE, the infrastructure gets out of the way so you can focus on the system you're building. The Bigger Picture Canvas redefines software development by shifting from writing static code to orchestrating living systems. Developers and AI co-create, observe, and evolve solutions in real time. Instead of building UIs for users, we build interactive environments for agents, turning debugging, testing, and execution into a continuous, visual feedback loop that accelerates innovation and brings ideas to production faster than ever. The Multi-Agent Dev Canvas we built here is one example. The pattern applies anywhere you're building agent-driven systems: AI orchestration, workflow automation, data pipelines, autonomous services. Anywhere you need to see, steer, and validate a complex system as it runs, that's where Canvas belongs. Resources copilot-canvas-runtime — this repository: the Multi-Agent Dev Canvas extension, scenario, and demo prompt GitHub Copilot Documentation — Official documentation for GitHub Copilot features Microsoft Foundry Documentation — Build and deploy AI agents with Microsoft FoundryMCP for Beginners: Why Every AI Engineer and Developer Should Learn the Model Context Protocol
If you have spent any time building with large language models in the last year, you have hit the same wall everyone hits: your model is brilliant at reasoning but blind to the real world. It cannot read your database, call your internal API, search your documents, or trigger a deployment unless you hand-write glue code for every single integration. The Model Context Protocol (MCP) exists to tear that wall down, and Microsoft's open-source MCP for Beginners curriculum (reachable via the short link https://aka.ms/mcp-for-beginners) is the most complete, hands-on way to learn it. This post explains what MCP is, walks through the latest updates to the course, shows real code, and makes the case for why MCP belongs on your learning roadmap right now. Whether you are an AI engineer shipping agents to production, a developer wiring tools into Copilot, or a student trying to build a standout portfolio project. What is MCP, and why does it matter? Think of MCP as a universal translator for AI applications. Just as a USB-C port lets you connect any peripheral to any laptop without a custom cable per device, MCP lets an AI model connect to any tool or data source through one standardized protocol. The course uses exactly this analogy, and it holds up well. Before MCP, integrations were an M × N problem: every one of your M AI applications needed bespoke code to talk to each of your N tools. MCP turns that into an M + N problem. Build a tool once as an MCP server, and any MCP-compatible client, Claude Desktop, VS Code, Cursor, GitHub Copilot, and many others — can use it immediately. The protocol is built on a clean client–server model with a small set of primitives: Tools — functions the model can call (query a database, send an email, run code). Resources — data the server exposes for context (files, records, documents). Prompts — reusable, parameterized prompt templates. Sampling — a server asking the client's LLM to generate a completion, enabling collaborative workflows. Elicitation — a server requesting structured input from the user mid-task. Roots — boundaries that tell a server which directories or resources it is allowed to operate on. Communication runs over JSON-RPC, with transports for local processes ( stdio ) and remote servers (streamable HTTP). That standardization is the whole point: write to the spec, and you interoperate with the entire ecosystem. What's new: the latest updates to the course The MCP for Beginners curriculum is actively maintained, and the public changelog reads like a release log for a living product. Here are the most important recent changes, drawn directly from that changelog. 1. Aligned to MCP Specification The biggest update: the entire curriculum has been validated against the current MCP Specification 2025-11-25 and the latest official SDKs. Stale references to older spec revisions (2025-03-26 and 2025-06-18) were corrected across the security, transport, real-time search, sampling, and stdio-server modules, with links repointed to the canonical modelcontextprotocol.io spec paths. A gap analysis confirmed the course already covers every primitive introduced or expanded in the latest spec: Sampling — covered in lesson 3.14 and Advanced Topics. Elicitation (including URL mode) — in Core Concepts and Protocol Features. Roots — in the Introduction, Core Concepts, and Root Contexts. Tasks (experimental, long-running operations) — in Core Concepts and Protocol Features. Tool Annotations ( readOnlyHint / destructiveHint ) — in Core Concepts and Protocol Features. 2. Samples validated against current SDKs Code that does not run is worse than no code at all, so the maintainers re-validated the core samples: TypeScript: @modelcontextprotocol/sdk resolved to 1.29.0 ; a tsc --noEmit type-check passed with no errors — the McpServer and StdioServerTransport APIs remain valid. Python: validated in an isolated virtual environment with mcp[cli] (1.27.2); FastMCP.list_tools() correctly returned the sample add and subtract tools. SDK version pins across labs were bumped (for example mcp>=1.26.0 ) and lockfiles regenerated so every sample tracks the current release. 3. A serious security pass Security is treated as a first-class concern, not an afterthought. A full audit across every dependency manifest and the sample source code was run, and npm audit now reports 0 vulnerabilities in every audited directory. Highlights: Transitive npm advisories (in the MCP Inspector dev tool, the OpenAI client, and the SDK) were remediated by bumping @modelcontextprotocol/inspector to 0.22.0 and pinning a patched shell-quote . A real code-level command-injection fix (OWASP A03): an open_in_vscode tool that used subprocess.run(..., shell=True) was rewritten to launch the resolved executable directly with no shell — closing a metacharacter-injection vector. Python dependencies were audited with pip-audit , and a vulnerable transitive werkzeug was pinned to a patched >=3.1.6 . For anyone learning to ship agents, this is gold: the course demonstrates the whole secure-development loop, not just the happy path. 4. New lessons and a growing curriculum The curriculum keeps expanding with practical, modern lessons: 5.17 Adversarial Multi-Agent Reasoning — two agents argue opposite sides of a question using shared MCP tools ( web_search + run_python ), judged by a third agent. Includes a Mermaid architecture diagram, orchestrators in Python, TypeScript, and C#, and use cases like hallucination detection, threat modeling, and API design review. 3.12 MCP Hosts — configuration for Claude Desktop, VS Code, Cursor, Cline, and Windsurf, with JSON templates and a transport comparison table. 3.13 MCP Inspector — a debugging guide for testing tools, resources, and prompts. 4.1 Pagination — cursor-based pagination patterns in Python, TypeScript, and Java. 5.16 Protocol Features — progress notifications, request cancellation, resource templates, and lifecycle management. 5. Microsoft product rebranding Content was updated to reflect Microsoft's rebranding: Azure AI Foundry → Microsoft Foundry, and the AI Toolkit (AITK) → Microsoft Foundry Toolkit Extension for VS Code. If you have seen older tutorials referencing the previous names, the curriculum is now current. Your first MCP server: see how little code it takes The course's "first server" lesson builds a simple calculator. Here is the shape of a minimal MCP server in Python using FastMCP , which mirrors the validated sample in the repo. Notice how the protocol plumbing disappears — you just decorate functions. # server.py — a minimal MCP server with two tools from mcp.server.fastmcp import FastMCP # Name your server; this identifies it to MCP clients mcp = FastMCP("Calculator") @mcp.tool() def add(a: int, b: int) -> int: """Add two numbers and return the result.""" return a + b @mcp.tool() def subtract(a: int, b: int) -> int: """Subtract b from a and return the result.""" return a - b if __name__ == "__main__": # Run over stdio so local hosts (VS Code, Claude Desktop) can connect mcp.run() The same idea in TypeScript, using the official SDK validated at version 1.29.0 : // server.ts — minimal MCP server in TypeScript import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; const server = new McpServer({ name: "Calculator", version: "1.0.0" }); // Register a tool with a typed input schema server.tool( "add", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }], }) ); // Connect over stdio and start listening const transport = new StdioServerTransport(); await server.connect(transport); That is a complete, runnable server. The docstrings and schemas matter: MCP exposes them to the model so it knows when and how to call each tool. Clear descriptions are effectively prompt engineering for your tools — a common pitfall is leaving them vague, which leads to the model misusing or ignoring the tool. Connecting it in VS Code Once your server runs, an MCP host connects to it. A typical VS Code / host configuration looks like this: { "servers": { "calculator": { "command": "python", "args": ["server.py"] } } } Lesson 3.12 (MCP Hosts) covers the equivalent JSON for Claude Desktop, Cursor, Cline, and Windsurf, and lesson 3.13 shows how to use the MCP Inspector to test your tools before wiring them into a host — the single best debugging habit you can build early. How the course is structured The curriculum is organized as a progressive journey with hands-on code in C#, Java, JavaScript, Python, Rust, and TypeScript. It is grouped into phases: Foundations (Modules 0–2): Introduction, Core Concepts, and Security. Building (Module 3): Getting Started — 15 lessons covering your first server and client, LLM clients, VS Code integration, stdio and HTTP streaming, testing, deployment, auth, hosts, the Inspector, sampling, and MCP Apps. Growing (Modules 4–5): Practical Implementation and Advanced Topics — 17 advanced lessons including Azure integration, OAuth2, Entra ID auth, scaling, multi-modality, context engineering, custom transports, and adversarial multi-agent reasoning. Mastery (Modules 6–11): Community Contributions, Lessons from Early Adoption, Best Practices, Case Studies, a Microsoft Foundry Toolkit workshop, and an end-to-end 13-lab PostgreSQL capstone. That final module is the standout for portfolio building: a complete, production-flavored path that takes you from architecture and row-level security through database design, a FastMCP server, semantic search with pgvector and Azure OpenAI, testing, Docker deployment to Azure Container Apps, and monitoring with Application Insights. Why developers should learn MCP now For AI engineers MCP is becoming the default integration layer for agents. Instead of re-implementing tool calling for every framework, you write to one open protocol and your tools work everywhere. The advanced modules — sampling, roots, elicitation, scaling, routing, and adversarial multi-agent patterns — are exactly the techniques you need to move agents from demo to production. For developers MCP is already wired into tools you use daily: VS Code, GitHub Copilot, Claude Desktop, Cursor, and more. Learning to build an MCP server means you can expose your systems — internal APIs, databases, CI/CD — to AI assistants safely. The security-first approach in the course (OAuth2, Entra ID, RBAC, dependency auditing) teaches you to do this the right way from day one. For students MCP is a rare opportunity to learn a technology while it is still early, with a free, beginner-friendly, Microsoft-maintained curriculum and code in six languages. The 13-lab capstone alone is a genuine portfolio project. And with content translated into 50+ languages, the barrier to entry is low no matter where you are. Responsible and secure by design A recurring theme worth calling out: the course does not treat security and governance as optional extras. It models real practices you should carry into your own work: Least privilege via roots — constrain what a server can touch. Tool annotations — mark tools readOnlyHint or destructiveHint so clients can warn users before destructive actions. No shells for user input — the command-injection fix is a textbook example of why you never pass untrusted input through a shell. Dependency hygiene — audit with npm audit and pip-audit , and pin patched releases. Proper auth — dedicated lessons on OAuth2 and Microsoft Entra ID. Key takeaways MCP standardizes how AI connects to tools and data, turning a combinatorial integration problem into a simple, reusable one. The course is current, validated against MCP Specification 2025-11-25 with SDKs at TypeScript 1.29.0 and Python mcp 1.27.2 . Samples actually run, and the repo demonstrates a full secure-development loop with 0 reported vulnerabilities after auditing. It is broad and deep: from a 10-line calculator server to a 13-lab production capstone, in six languages. It is the fastest credible path to MCP fluency for AI engineers, developers, and students alike. Get started today Open the course: https://aka.ms/mcp-for-beginners (redirects to the GitHub repository). Fork and clone it — use a sparse checkout to skip translations for a faster download: git clone --filter=blob:none --sparse https://github.com/microsoft/mcp-for-beginners.git cd mcp-for-beginners git sparse-checkout set --no-cone "/*" "!translations" "!translated_images" Build your first server with lesson 3.1 in your language of choice. Debug it with the MCP Inspector, then connect it in VS Code. Go deep with the 13-lab database capstone, and read the official spec at modelcontextprotocol.io. Track what's new in the changelog and join the community discussions. MCP is quietly becoming the connective tissue of the AI ecosystem. The earlier you learn it, the more leverage you will have — and Microsoft's MCP for Beginners is the clearest on-ramp available. Star the repo, build a server this week, and start connecting your AI to the world.Mastering Query Fields in Azure AI Document Intelligence with C#
Introduction Azure AI Document Intelligence simplifies document data extraction, with features like query fields enabling targeted data retrieval. However, using these features with the C# SDK can be tricky. This guide highlights a real-world issue, provides a corrected implementation, and shares best practices for efficient usage. Use case scenario During the cause of Azure AI Document Intelligence software engineering code tasks or review, many developers encountered an error while trying to extract fields like "FullName," "CompanyName," and "JobTitle" using `AnalyzeDocumentAsync`: The error might be similar to Inner Error: The parameter urlSource or base64Source is required. This is a challenge referred to as parameter errors and SDK changes. Most problematic code are looks like below in C#: BinaryData data = BinaryData.FromBytes(Content); var queryFields = new List<string> { "FullName", "CompanyName", "JobTitle" }; var operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, modelId, data, "1-2", queryFields: queryFields, features: new List<DocumentAnalysisFeature> { DocumentAnalysisFeature.QueryFields } ); One of the reasons this failed was that the developer was using `Azure.AI.DocumentIntelligence v1.0.0`, where `base64Source` and `urlSource` must be handled internally. Because the older examples using `AnalyzeDocumentContent` no longer apply and leading to errors. Practical Solution Using AnalyzeDocumentOptions. Alternative Method using manual JSON Payload. Using AnalyzeDocumentOptions The correct method involves using AnalyzeDocumentOptions, which streamlines the request construction using the below steps: Prepare the document content: BinaryData data = BinaryData.FromBytes(Content); Create AnalyzeDocumentOptions: var analyzeOptions = new AnalyzeDocumentOptions(modelId, data) { Pages = "1-2", Features = { DocumentAnalysisFeature.QueryFields }, QueryFields = { "FullName", "CompanyName", "JobTitle" } }; - `modelId`: Your trained model’s ID. - `Pages`: Specify pages to analyze (e.g., "1-2"). - `Features`: Enable `QueryFields`. - `QueryFields`: Define which fields to extract. Run the analysis: Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, analyzeOptions ); AnalyzeResult result = operation.Value; The reason this works: The SDK manages `base64Source` automatically. This approach matches the latest SDK standards. It results in cleaner, more maintainable code. Alternative method using manual JSON payload For advanced use cases where more control over the request is needed, you can manually create the JSON payload. For an example: var queriesPayload = new { queryFields = new[] { new { key = "FullName" }, new { key = "CompanyName" }, new { key = "JobTitle" } } }; string jsonPayload = JsonSerializer.Serialize(queriesPayload); BinaryData requestData = BinaryData.FromString(jsonPayload); var operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, modelId, requestData, "1-2", features: new List<DocumentAnalysisFeature> { DocumentAnalysisFeature.QueryFields } ); When to use the above: Custom request formats Non-standard data source integration Key points to remember Breaking changes exist between preview versions and v1.0.0 by checking the SDK version. Prefer `AnalyzeDocumentOptions` for simpler, error-free integration by using built-In classes. Ensure your content is wrapped in `BinaryData` or use a direct URL for correct document input: Conclusion Using AnalyzeDocumentOptions provides a cleaner and more reliable way to work with query fields in Azure AI Document Intelligence using C#. By aligning with the latest SDK approach, developers can simplify implementation, reduce common errors, and improve code maintainability. Keeping up with SDK enhancements and recommended practices ensures more accurate and efficient document data extraction. As Azure AI capabilities continue to evolve, adopting modern integration patterns will help you build scalable and future-ready document processing solutions with greater confidence. Reference Official AnalyzeDocumentAsync Documentation. Official Azure SDK documentation. Azure Document Intelligence C# SDK support add-on query field.
Microsoft Leads a New Era of Software Supply Chain Transparency
Microsoft announces the general availability of Microsoft’s Signing Transparency (MST) – a first-of-its-kind capability that brings unprecedented visibility and trust to our software supply chain. With this release, Microsoft is leading the industry by recording the build of critical cloud services into a publicly readable and verifiable SCITT standard (Supply Chain Integrity, Transparency, and Trust) compliant ledger. This means every production software build for in scope services like Azure Attestation and Azure Managed HSM (Hardware Security Module), Azure confidential ledger, Microsoft Signing Transparency itself (and others over time) – is now logged in an immutable, tamper-evident record. Only builds that are in the MST ledger are deployed to production; this gives customers confidence that the supply chain for these critical services can be audited at anytime. Notably, the MST ledger is fully open source and built to align with the emerging IETF SCITT standard. By embracing SCITT’s principles and open protocols, Microsoft ensures that MST not only secures our own ecosystem but also contributes to a broader industry movement toward standardized supply chain transparency. The open-source MST ledger serves as a verifiable trust anchor that any organization or researcher can inspect, audit, or even integrate with their own tooling. MST itself meets the highest levels of transparency, backed by a tamper-proof confidential ledger, open-source, and independently verified. Specifically, we are making the foundation of our trust model transparent and accessible to everyone – reinforcing that trust must be earned through proof, not just promises. This launch marks a major milestone in our commitment to Zero Trust principles, extending “never trust, always verify” all the way into the build itself. Building on a public preview introduced late last year, MST’s general availability delivers verifiable transparency at the software level. It transforms traditional code signing with an additive trust layer that is accessible via an open verification model. Every new software update is accompanied by a publicly auditable proof of integrity, enabling security teams to proactively confirm that each update is authentic and unaltered. To help organizations get the most out of this capability, we are also introducing a free tool to explore the contents – Ledger Explorer – an offline tool that allows security teams to examine MST ledger entries, verify cryptographic proofs, and even validate the ledger’s integrity independently. This tool, combined with MST’s open design, ensures that every Microsoft customer – and the broader community – can hold us accountable in real time for the software we run on their behalf. Key Benefits of Microsoft’s Signing Transparency (MST) Verified Code Integrity – Every software release is cryptographically logged in MST’s ledgers. This makes each build tamper-evident and traceable. If an attacker attempts to inject malicious code or sign an unauthorized update, it will be evident through the well-defined validation step built into the SCITT standard. Organizations gain the assurance that code integrity can be independently confirmed at any time. Independent Verification & Zero Trust – MST enables customers and auditors to verify software authenticity on their own, without having to solely rely on vendor attestations. For each update, Microsoft provides a transparency “receipt” (proof of logging) that you can use to prove the update was officially published and unaltered. This fosters a “don’t just trust, verify” approach, empowering security teams to double-check everything running in their environment aligns with what Microsoft intended. Audit-Trail & Compliance – The transparency ledger creates a permanent, auditable timeline of code deployments. Every entry is a record of what was released and when, backed by cryptographic proofs. This simplifies compliance reporting and accelerates forensic analysis. In the event of an incident, you can quickly audit the ledger to see if any unexpected code was introduced. For highly regulated industries, MST offers concrete evidence of software integrity and policy compliance over time. Leadership & Open Standards – We are delivering real transparency now, encouraging a future where all critical software is released with verifiable integrity. MST’s open source implementation and SCITT-compliant design exemplify our commitment to openness and collaboration. We believe widespread adoption of these standards will strengthen supply chain security for everyone, making trust verification a universal practice. Next Steps Microsoft’s Signing Transparency is more than a new security feature and shapes the advances in trust technology. As threats grow more sophisticated, we must evolve the way we assure our customers about the software they depend on. With MST now generally available, we are leading by example: proving that it is possible to open up the traditionally opaque process of software deployment and turn it into a source of strength and trust, i.e. empowering each person with verifiable transparency. We invite the industry to join us on this journey and get started by reading the documentation and exploring Ledger Explorer today! Together, by embracing transparency and open standards, we can turn “trust but verify” from a slogan into an everyday reality for digital infrastructure.Using Keycloak with Azure AD to integrate AKS Cluster authentication process
Integrating Azure Kubernetes Service (AKS) with Keycloak through Azure Active Directory (Azure AD) as an intermediary leverages Azure AD’s support for OpenID Connect (OIDC) to handle authentication and authorization. This integration enhances security, streamlines user management, and simplifies the authentication process for users accessing the AKS cluster.Enhancing Data Security and Digital Trust in the Cloud using Azure Services.
Enhancing Data Security and Digital Trust in the Cloud by Implementing Client-Side Encryption (CSE) using Azure Apps, Azure Storage and Azure Key Vault. Think of Client-Side Encryption (CSE) as a strategy that has proven to be most effective in augmenting data security and modern precursor to traditional approaches. CSE can provide superior protection for your data, particularly if an authentication and authorization account is compromised.
