Azure Functions Agents

Use this skill to build serverless agents on Azure Functions with the Azure Functions serverless agents runtime. Align terminology and flows with the Microsoft Learn quickstart "Build serverless agents using Azure Functions": agents are defined in markdown files, runtime defaults live in agents.config.yaml, remote MCP servers live in mcp.json, and deployment uses azd.

Current Defaults

• Scaffold with Microsoft Foundry as the model provider.
• Scaffold files may keep gpt-4.1 as the safe Bicep fallback, but before provisioning or deploying, check the user's subscription/region for the best deployable GPT model and quota.
• Prefer the newest deployable GPT reasoning model with remaining quota when available, such as gpt-5.4 or newer. Only configure reasoning settings for models known to support them. Older models can fail if AZURE_FUNCTIONS_AGENTS_REASONING_* settings are present.
• For gpt-5.x and other reasoning-capable models, use reasoning effort medium by default, high when the task needs deeper reasoning or the agent is underthinking, xhigh only when the selected model supports it, and reasoning summary concise.
• If an agent needs web browsing, current public data, browser automation, data analysis, or code execution, use Azure Container Apps dynamic sessions. Do not write custom web-fetch tools for those cases.
• Use remote HTTP MCP servers and connection MCP servers from mcp.json; local stdio MCP servers are not supported by the runtime. When the user brings a stdio MCP server, convert the needed actions into Python custom tools or use a hosted remote MCP endpoint.
• Use Connector Namespaces, represented as Microsoft.Web/connectorGateways resources in Bicep.
• Generated apps should include a ready-to-edit src/local.settings.json, not only a template.
• Generated requirements.txt should use azurefunctions-agents-runtime[monitor] whenever the app includes APPLICATIONINSIGHTS_CONNECTION_STRING (the scaffolded infra provisions it by default), so the runtime auto-exports agent.run and dynamic_session.execute spans to Application Insights with no extra code. If the retrieved template ships the base azurefunctions-agents-runtime, add the [monitor] extra. When the app has no Application Insights, use the base package and either prompt the user to add it or omit telemetry.

Progressive References

Load only the files needed for the task:

Assess the Workspace

Before editing, inspect the app. Look for:

• src/function_app.py importing create_function_app()
• src/*.agent.md
• src/agents.config.yaml
• src/mcp.json
• src/tools/ and src/skills/
• src/local.settings.json
• infra/main.bicep, infra/app/*.bicep, infra/main.parameters.json
• azure.yaml
• .azure/*/config.json for existing azd environments

For existing apps, read the current files and preserve the app's structure. Current apps should use explicit builtin_endpoints, trigger.args, Foundry provider settings, agents.config.yaml for shared runtime defaults, and Connector Namespace MCP entries in mcp.json.

When inspecting deployed connectors, remember that Connector Namespace resources live under Microsoft.Web/connectorGateways. Connections are connectorGateways/<gateway>/connections, MCP server configs are connectorGateways/<gateway>/mcpserverconfigs, and trigger configs are connectorGateways/<gateway>/triggerconfigs. Do not search for legacy top-level Microsoft.Web/connections to find Connector Namespace connections.

Discuss and Plan the Agent

When the user says they want to create an agent, do not jump straight to files unless the request already contains enough detail. First learn enough to shape the app and discuss a short plan.

Ask only the questions needed to move forward, usually covering:

• Goal: What should the agent accomplish, and what should a successful run produce?
• Invocation: Should it be chat/API-driven, scheduled, event-triggered, connector-triggered, or some combination?
• Inputs and outputs: What data starts the run, and should the result be returned, logged, emailed, posted, drafted, stored, or sent somewhere else?
• Interactive surfaces: Does the user want to chat with the agent, expose a chat/API surface, stream responses, or expose the agent as a built-in MCP tool? Enable built-in endpoints only for those interactive scenarios.
• Tools and services: Does it need web/code execution, Microsoft Learn, Office 365, Teams, Azure Resource Manager, storage, queues, databases, or custom APIs?
• Teams targets: If the agent needs a Teams team, channel, or chat target, ask the user to paste a Teams link and parse IDs from it on their behalf. Do not ask them to manually find raw team IDs, channel IDs, or chat IDs. Load connector-teams.md before wiring Teams posting or diagnosing a missing Teams message.
• Safety boundaries: For actions like email, Teams posts, ticket creation, or resource changes, should the agent draft, ask for confirmation, or act automatically?
• Model selection: Which Azure subscription and region should be checked for GPT model access and quota? If the user has not specified them, use the current Azure CLI subscription and the planned AZURE_LOCATION.
• Deployment preference: Default to building and deploying to Azure with azd up, but offer to run locally first if the user wants a local iteration loop.

Do not ask the user to estimate runtime duration unless the workflow is obviously unusual or may run longer than the standard background-agent defaults. Set practical timeouts yourself: keep simple chat/API agents at the runtime default, set timer, connector-triggered, queue, and other background agents to 30 minutes, and align host.json functionTimeout to the longest agent timeout. For synchronous HTTP work that may take longer than about 230 seconds, choose a background/deferred pattern instead of relying on a longer HTTP response.

Do not add builtin_endpoints or a main.agent.md debug/chat agent just to test a scheduled, timer, connector-triggered, queue, or other background workflow. If the user asks for a chat bot, debug chat UI, chat API, streaming API, or says they want to talk to the agent, enable the appropriate built-in endpoints for that agent or add a separate interactive agent. Otherwise, verify background agents with the admin invoke endpoint and Application Insights.

After the discovery, summarize the plan before editing. Include the agent files, trigger or built-in endpoints, model choice, tools/MCP/connectors, infrastructure changes, and how it will be tested. If the user has already provided enough detail, make reasonable choices and proceed.

Scaffold a New App

Use manifest discovery + MCP primary retrieval when Azure MCP tools are available:

1. Fetch the Azure Functions template manifest from https://cdn.functions.azure.com/public/templates-manifest/manifest.json and find ai-serverless-agents-python. Use its catalog metadata (priority, categories, tags, whatsIncluded) to explain why this is the correct serverless agents scaffold. Keep repositoryUrl, folderPath, and gitRef for fallback.
2. Call Azure MCP functions_template_get with language: python and template: ai-serverless-agents-python. This is the primary source for the complete project files.
3. Write the returned files array into the target project. If the output is truncated or saved to a temporary file, read the complete JSON response before deciding retrieval failed.
4. If MCP returns an actual tool error, cannot retrieve the template, or returns an empty/zero-file result after reading the complete response, fall back to the manifest repositoryUrl, folderPath, and gitRef using direct GitHub download first and git clone --depth 1 only if downloads fail. Tell the user that MCP retrieval failed and GitHub fallback was used.

When Azure MCP tools are not available, skip directly to the manifest/GitHub fallback above. Do not invent a project structure from memory.

Do not use bundled template files as a scaffold source. This skill intentionally does not include a full copy of the serverless agents template because the template evolves independently; always retrieve it from MCP first or from the manifest GitHub coordinates as fallback.

After scaffolding from MCP or GitHub, tailor the app to the user's agent.

Baseline structure:

<project-root>/
  azure.yaml
  infra/
    abbreviations.json
    main.bicep
    main.parameters.json
    app/
      api.bicep
      connector-gateway.bicep
      trigger-config.bicep        # if using connector triggers
      foundry.bicep
      rbac.bicep
      session-pool.bicep
      session-pool-rbac.bicep
  src/
    function_app.py
    host.json
    local.settings.json
    requirements.txt
    .funcignore
    agents.config.yaml
    main.agent.md              # only when the user wants chat/API/MCP endpoints
    <agent-name>.agent.md
    mcp.json

Default scaffold choices:

• Python 3.13 in Bicep.
• Foundry provider. Keep gpt-4.1 in template defaults only as a safe fallback.
• Before running azd provision or azd up, run the model and quota checks in models.md, recommend the best deployable GPT model, and set the FOUNDRY_MODEL, FOUNDRY_MODEL_NAME, FOUNDRY_MODEL_VERSION, FOUNDRY_DEPLOYMENT_CAPACITY, and reasoning env vars when a better reasoning-capable model is selected.
• No reasoning app settings unless the app is explicitly upgraded to a reasoning-capable model.
• ACA dynamic session pool when any agent needs code execution or web browsing.
• Optional Office 365 Outlook connection MCP server when TO_EMAIL is set.
• Built-in endpoints only when the scenario is interactive. Do not add debug chat UI, chat API, streaming API, built-in MCP, or a general main.agent.md to a scheduled-only/background-only app unless the user asked for that surface.
• Explicit timeout settings: use functionTimeout: "00:30:00" in host.json for background-capable apps, keep simple chat/API agents at timeout: 900, and set timer, connector-triggered, queue, or other long-running background agents to timeout: 1800 unless the task clearly needs more.
• Connector-triggered apps use the preview extension bundle and a second-step trigger config deployment after the connector_extension system key exists.
• local.settings.json includes local storage and Foundry/MCP placeholders.

After copying, remove or adjust sample-specific agents and instructions. Keep infrastructure modules only when the app needs them.

Local Development First Rule

Default to deploying new apps to Azure with azd up; these agents are designed for managed identity, Foundry, dynamic sessions, Connector Namespaces, Application Insights, and Functions hosting. Offer to run locally first when the user wants to iterate before deploying, and explain the local tools required.

Local prerequisites:

• Azure Developer CLI (azd)
• Azure Functions Core Tools v4
• Python 3.13+
• Azurite, when using AzureWebJobsStorage=UseDevelopmentStorage=true
• Azure CLI login for local managed-identity-equivalent access

When an app uses Foundry, dynamic sessions, or connector MCP servers, run azd provision before local development. Local func start still calls real Azure resources for model, session pool, and connector operations.

Local loop:

1. azd init
2. azd provision
3. Copy outputs from azd env get-values into src/local.settings.json.
4. Start Azurite with azurite --skipApiVersionCheck.
5. From src, create a venv, install requirements, and run func start.

The scaffolded Bicep grants the deployer/local user access to Foundry and the session pool, and connection access policies when optional connectors are enabled.

Build or Modify Agents

Each .agent.md file defines one agent. Use YAML frontmatter for runtime configuration and markdown for behavior. The file stem becomes the function name and built-in endpoint route segment.

Choose endpoints from the scenario, not from testing convenience. Scheduled-only, timer, connector-triggered, queue, and other background agents should omit builtin_endpoints unless the user asks to talk to that agent, expose it as an API, or expose it as a built-in MCP tool. Use the admin endpoint and Application Insights to test background agents.

Current trigger example:

---
name: Daily Report
description: Sends a daily report.

trigger:
  type: timer_trigger
  args:
    schedule: "0 0 15 * * *"

mcp: true
---

Current built-in endpoint example:

---
name: Chat Agent
description: Interactive agent for testing.

builtin_endpoints:
  debug_chat_ui: true
  chat_api: true
  mcp: true

mcp: false
---

Load agent-files.md before adding less common frontmatter fields.

Model Selection Guidance

For new scaffolds, keep gpt-4.1 as the safe Bicep default, but actively try to select the best deployable GPT model before provisioning. Use az cognitiveservices model list and az cognitiveservices usage list to check the user's subscription, target region, model version, deployment SKU, and quota. See models.md for copyable commands.

Do not silently deploy gpt-4.1 just because it is the template default. Either run the model and quota checks and choose/recommend a better deployable model, or state why the checks could not be run and then use gpt-4.1 without reasoning settings.

Recommend the newest deployable GPT reasoning model with remaining quota. Ask the user to choose when there are meaningful tradeoffs, such as newer/slower/costlier reasoning models versus smaller mini/nano models. If no reasoning-capable model has quota, or if availability/quota discovery cannot determine what the user can deploy, use gpt-4.1 without reasoning settings.

When selecting a reasoning-capable model, confirm reasoning support from the Azure OpenAI reasoning models documentation, then set model and reasoning values together. Use reasoning effort medium by default. Offer high and increased deployment capacity when the task needs deeper reasoning or the deployed agent is underthinking. Use xhigh only when the selected model supports it and the user wants maximum reasoning with possible latency/cost tradeoffs. Use reasoning summary concise.

Deploy and Verify

For new apps, use azd up from the project root. If the user has agreed to deploy, run the deployment commands yourself instead of stopping after printing them. Set required azd env vars, run azd init when needed, run model/quota checks before provisioning, confirm the active Azure subscription, then run azd up with a generous timeout. Pause only for information you genuinely need from the user, such as a missing recipient email, region choice, subscription confirmation, or portal-only connector authorization. Do not route secrets through chat.

Before azd provision or azd up, run az account show --query "{name:name,id:id,tenantId:tenantId}" and show the selected subscription to the user unless they already explicitly named the subscription for this deployment. If it is not the intended subscription, have the user choose or run az account set --subscription <subscription-id> before provisioning. Do not deploy to whichever Azure CLI subscription happens to be active without making that choice visible.

Unless the user explicitly asks for continuous deployment, deploy from the local workspace with azd up. Do not create GitHub Actions workflows, CI/CD pipeline files, repository secrets, or run azd pipeline config for a normal app deployment request.

After deployment, verify outputs with azd env get-values, open or provide the relevant app URL, show the user how to get the default function key only when built-in chat UI/API endpoints are present, open Connector Namespace authorization links when connectors are present, check connection status, and run a smoke test when practical. For timer or other non-HTTP agents, manually trigger the function with the admin endpoint after deployment, then query Application Insights requests, traces, and exceptions for that run. For built-in chat agents, open or provide the /agents/<slug>/ URL and call the chat API if useful. Do not rely on az functionapp log tail for Flex Consumption agent diagnostics.

When a manual trigger starts a timer/background agent, tell the user what to expect before waiting: the admin endpoint often returns 202 Accepted, the agent may run for several minutes, and Application Insights can lag. Give short progress updates while checking telemetry, especially for runs that take 5-10 minutes. If telemetry does not appear after a reasonable wait, or the function appears stuck past its expected duration, say so and switch to troubleshooting instead of silently waiting or repeatedly triggering the function.

For connector actions with visible side effects, such as Teams posts or Outlook messages, verify the downstream side effect after the first run. If the user reports it is missing, do not keep triggering the agent. Inspect the recorded tool result/session and run a focused connector smoke test with the same arguments before changing model settings or rerunning the full workflow.

Be hands-on after scaffolding. Do not stop at a command list when the next command is safe and the user already approved the direction. Run azd up, open authorization URLs, run azd env get-values, test deployed endpoints, and report the results. Stop only for user-only actions such as signing in to authorize a connector, selecting an ambiguous option, or entering secrets.

When creating backing Azure resources beyond this skill's bundled Bicep, use Azure docs. If the agent needs searchable docs context, ask the user to connect the Microsoft Learn MCP server at https://learn.microsoft.com/api/mcp.

Useful endpoints:

• Chat UI, when enabled: /agents/<agent-file-stem>/
• Chat API, when enabled: POST /agents/<agent-file-stem>/chat
• Streaming chat API, when enabled: POST /agents/<agent-file-stem>/chatstream
• MCP endpoint, when enabled: /runtime/webhooks/mcp
• Manual non-HTTP trigger: POST /admin/functions/<agent-file-stem>