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Foundry Agentic Loop

GitHubOfficialFirst committed by vieiraaeDistributed by GitHub

  • Skill
  • Workflow
  • Software development
  • MIT
  • Low risk
  • Has scripts
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From the author

Build agentic solutions on Microsoft Foundry, GitHub and Azure: hosted agents, toolbox tools and skills, Foundry IQ grounding and a React/FastAPI app. Three modes: rapid prototyping in an existing Foundry project; spec-driven development (SDD), where the foundry-agentic-loop-workflow runs Specify, Implement, Verify and Deploy to create the Foundry project and deploy with azd, or one phase runs alone; and Improve, which refines this skill and the workflow from past runs. Use for Agentic Loop requests and canvas actions, or end-to-end agent solutions from architecture to Azure deployment. Not for isolated Azure administration or unrelated UI edits.
— the description by vieiraae

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

foundry-agentic-loop provides a structured framework for developing agentic applications. It supports two primary modes: rapid prototyping for existing projects and Spec-Driven Development (SDD) for new projects requiring full Azure deployment pipelines. The skill manages the lifecycle of agents, toolbox tools, Foundry IQ grounding, and web application components, ensuring consistent architecture, security, and observability across the development process.

What it does

  • Orchestrates end-to-end agentic solution development from architecture to Azure deployment.
  • Provides a Spec-Driven Development workflow covering Specify, Implement, Verify, and Deploy phases.
  • Supports rapid prototyping for existing Foundry projects.
  • Manages Foundry hosted agents, toolbox tools, and Foundry IQ grounding configurations.
  • Automates deployment of React/FastAPI applications to Azure Container Apps.
  • Enforces security best practices through managed identities and least-privilege RBAC.

When to use it

  • When building new agentic solutions on Microsoft Foundry that require a structured development lifecycle.
  • When you need to deploy agentic applications to Azure using standardized pipelines.
  • When prototyping agentic features within an existing Foundry project.
  • When you need to integrate Foundry IQ grounding or custom toolbox tools into an agent.

Advantages

  • Ensures architectural consistency through a contract-first development approach.
  • Automates complex deployment tasks using azd and Bicep infrastructure layers.
  • Provides built-in observability via OpenTelemetry and Application Insights integration.
  • Maintains project history and lessons learned to improve future development cycles.
  • Reduces manual configuration errors by enforcing standardized workspace structures.

Usage examples

“Start a new agentic project using the SDD workflow.”
The agent initializes the SDD workflow, creates the necessary project structure, and begins the Specify phase to define the project requirements.
“Deploy my current Foundry project to Azure.”
The agent validates the current configuration, runs the deployment pipeline, and reports the status of the Azure Container Apps deployment.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/foundry-agentic-loop/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip foundry-agentic-loop.zip -d .agents/skills

Documentation by vieiraae

Foundry Agentic Loop

Coordinate the solution and use microsoft-foundry for platform operations. Explicit user choices override defaults. Reuse known context, ask only for missing decisions, read only the references a step needs, and report decisions, blockers and endpoints concisely.

Bootstrap (once per session)

  1. Foundry skill. If microsoft-foundry is unavailable, ask, then install it (copilot plugin install azure@awesome-copilot, or gh skill install microsoft/azure-skills microsoft-foundry --agent github-copilot --scope project) and reload skills.

  2. Canvas and workflow. This plugin ships foundry-agentic-loop-canvas and foundry-agentic-loop-workflow, listed as foundry-agentic-loop:<name> or <name>. Call extensions_manage with operation: "list". Never install extensions from a URL and never edit Copilot settings files. If one is listed under neither name, tell the user which is missing and that it ships with the plugin, then ask before running copilot plugin install foundry-agentic-loop@awesome-copilot; after they approve, call extensions_reload once. If one is disabled, tell the user to re-enable it in their extension settings and continue in a new session: running sessions keep it disabled, even after a reload. If one failed, inspect it and report its log. Without the canvas, continue in Chat; without the workflow, SDD cannot start, so offer rapid prototyping.

  3. Canvas. When work starts from Chat, open it once: open_canvas with canvasId: "foundry-agentic-loop-canvas", instanceId: "foundry-agentic-loop" and input: { mode }. Prompts that begin [Agentic Loop canvas action] come from the canvas; follow their target, approval and resume instructions literally.

Choose the mode

SignalMode
Root AGENTS.md has an Agentic Loop sectionIts mode. Switch only when the user confirms, then rewrite the section
Canvas prompt or explicit choiceAs stated
Existing Foundry project selected or named; fast prototype; no deployment pipeline neededRapid prototyping: rapid.md
Spec, new Foundry project, azd or IaC, CI-ready repo or full Azure deploymentSDD: sdd.md
Improve, learn from runs, or the canvas improve promptImprove: improve.md; measure changes with node scripts/evals.mjs (index, then run --base <commit>; exit 2 means evals are unavailable, so stop)
One named SDD phase, with no run activeIts guide (Specify, Implement, Verify or Deploy) with the SDD defaults

Otherwise ask once with the choices "Rapid prototyping (existing Foundry project)" and "SDD (new project, deployed with azd)".

Lock it in. Before any other workspace write (in the rapid preflight, or just before the SDD workflow starts), upsert the section from agents-md-rapid.md or agents-md-sdd.md into the root AGENTS.md: replace everything between its foundry-agentic-loop begin and end markers, or append it, keeping other content; then read it back. The section routes short commands such as deploy and locks the mode for Chat, the canvas and the workflow. Azure settings go only in .azure/deployment-plan.md, never in AGENTS.md. Improve mode never writes it.

Defaults

  • Agents: Foundry hosted agents exposing the Responses API, Python 3.13, code deployment. Create them through microsoft-foundry's foundry-agent/create/quick-start-hosted.md with the Basic sample, resolved through that skill, never a guessed URL. Use its advanced workflow only when quick-start cannot support a capability, keeping these defaults. Prompt agents only when requested.

  • Framework: the Copilot SDK for agents that use runtime skills (recipe, reference agent); Microsoft Agent Framework for basic agents and explicit workflows. If a workflow also needs skills, clarify. One framework per agent.

  • Models: rapid keeps the Basic sample's model deployment, or verifies and reuses a supplied one; SDD defaults to gpt-5.4-mini unless the spec needs otherwise. Run the joint placement preflight in foundry-models.md before creating resources. Never hard-code a replacement or silently duplicate a deployment.

  • Tools and knowledge: one governed Foundry toolbox for built-in tools, MCP connections and agent skills; agents use its endpoint, not hard-coded tool URLs. Private knowledge uses Foundry IQ through the toolbox, with citations for supported claims and a deterministic insufficient-evidence response; test both. Add Search only when needed. Reuse run skills the user names from the run catalog instead of regenerating them.

  • App: React + Vite, FastAPI and FastMCP (streamable HTTP) on Azure Container Apps. Rapid deploys with az containerapp up --name <app> --source <dir> --resource-group <rg> --environment <env>; SDD uses azd with a microsoft.foundry layer plus a Bicep (AVM) app layer, remoteBuild: true and one ACR pulled by managed identity. Add services from the reference architecture only when the solution needs them.

  • App access: decide it from the data. When everything the app shows is sample, synthetic or deliberately public, default to no end-user sign-in, even for staff users; services still authenticate with managed identities and need no anonymous-session or identity-verification workarounds. When it shows private, sensitive or user-specific data, ask for the access model before deploying and default to internal ingress or Entra sign-in; anonymous access needs explicit approval. With sign-in, the API pre-authorizes the SPA, the spec states how automated browser tests get a user token, and Implement proves that token path as soon as the app registrations exist.

  • Identity and safety: managed identities and least-privilege keyless RBAC (contract; preflight with check_rbac_preflight.py). Check management-plane, data-plane and role-assignment rights separately; subscription Owner does not grant Foundry data-plane roles. Plan roles for identities not yet created and verify actual principals after creation. No credentials in browser code or Git; Foundry guardrails on.

  • Observability: OpenTelemetry from the backend, agents, MCP servers and model calls into one Application Insights connected to the project (observability.md). Sensitive content capture stays off unless approved with redaction and retention controls.

Workspace files

FileHolds
AGENTS.md (root)The mode lock and short-command routes; never Azure settings
README.md (rapid) or docs/spec.md (SDD, from the spec template)The contract
.azure/deployment-plan.mdEvery Azure setting, in both modes (template)
.env (Git-ignored)What the canvas reads: FOUNDRY_PROJECT (full ARM ID, once validated or created), LOCAL_FRONTEND and DEPLOYED_FRONTEND (verified URLs only). Upsert, keep other keys and read back
docs/LOOP.mdLessons (template)
.agentic-loop/runs/<runId>.jsonSDD flight records, written by the workflow
.devcontainer/devcontainer.jsonOptional dev container (asset)

Working rules

  • Contract first. Freeze service names, environment variables, protocols, endpoints, schemas, auth, streaming, errors and versions (implementation-contract.md) before parallel work.

  • Integration first. Pass the hosted integration checkpoint with a minimal agent before building full orchestration, UI or backend: always a model request, plus a toolbox skill load and a safe MCP call only when the architecture needs them. Record excluded capabilities as N/A with the reason, never as passed, and never add unused ones to satisfy the checkpoint.

  • Evidence, not signals. HTTP 200, exit 0 or active never pass alone; require terminal completion, schema-valid output, execution evidence and correlated traces. Fix with bounded diagnostics, not unchanged redeployments.

  • Wait by completion. Provisioning, deployments, role-assignment propagation and agent readiness end when their command or status check says so: run the command synchronously with a long initial wait, or poll its status every 30-60 s against a stated deadline. Never chain fixed sleeps; keep coding while Azure works.

  • Subagents. Before the checkpoint, parallelize only research and preflight. After it, give each worker a bounded goal, exclusive workspace-relative files or folders, skill references, verified context, the contract, acceptance checks and stop conditions. One owner (the coordinator, or the SDD phase lead) holds the contract and the shared files: azure.yaml, infra/, docs/, .env, .azure/, .agentic-loop/, AGENTS.md and the root lockfiles. Keep teams small, avoid recursive delegation, serialize shared Azure mutations and integrate centrally; worker success is not acceptance. Delegation grants no extra approval. Without subagents, work sequentially and say so.

  • Safety. Reuse resources; confirm billable, security or compliance changes; delete without approval only a resource this build created whose provisioning failed; never sign in for the user; never report an unverified deployment as successful.

  • Workspace boundary. Builds stay inside the workspace: read lessons only from its own docs/LOOP.md, scaffold from the Foundry samples and these references, and use code from other folders only when the user names them. Only improve mode reads past runs elsewhere.

  • Work IQ context. Only when the user or a canvas prompt asks for it, query the workiq MCP server before clarifying questions, fold in only the requirements that matter, cite each by source type and confirm them. Never copy confidential content, personal data or secrets into the workspace; if Work IQ is unavailable or needs consent, say so and continue without it.

  • Companions and SDKs. Verify installed SDK APIs against the latest stable release (or the newest stable one the build's package index serves, recording the gap) and lock versions before generating code; package sources beat stale guidance. Consult the build catalog only for needed capabilities; respect pins, ask before installs and route third-party skill defects upstream.

Lessons

Right after a fix is verified (the failing probe or an equivalent boundary check reruns successfully; an edit, commit or deployment alone is not resolution), upsert a lesson in the workspace's docs/LOOP.md from the template before unrelated work, then read it back. Keep user notes, update matching lessons instead of duplicating them, and never include secrets, raw .env values or payloads. The coordinator, or the SDD phase lead, owns writes; workers report evidence. Lessons never authorize skill edits or weaker gates; improve mode applies them with approval.

Files

references/verify.md

Verify phase

Prove that the implementation meets docs/spec.md before anything is exposed publicly. Apply the skill defaults, and use microsoft-foundry for agent operations. On a rerun, re-read docs/verify.md and rerun only the checks that are not passed, plus anything their fixes touch.

Until Deploy is approved, run the frontend and backend locally and never deploy them, even with internal ingress. Redeploy a hosted agent, or an MCP server it calls, only when its code changed. Infra fixes may re-provision once app images come from ${SERVICE_<NAME>_IMAGE_NAME=} (image parameters), but must not add public ingress.

Run every azd command from the workspace root, with AZURE_DEV_USER_AGENT=microsoft_foundry_skill set inline.

1. Preflight

  • azure.yaml: every agent has kind: hosted, protocols, and a codeConfiguration whose entryPoint exists. Every Container Apps name is 32 characters or fewer.

  • Infra: az bicep build --file infra/app/main.bicep succeeds.

  • Dependencies: they are locked.

  • Local config: wire .env and any local settings from azd env get-values. Show key names only, never values. Secrets resolve through managed identity, never as literals.

2. Run locally

  1. Start each component in the background with telemetry on:

    • MCP servers

    • the backend

    • the frontend

    • azd ai agent run --no-client for each agent

    Poll readiness with a timeout. If a component fails to start, stop and report its exit code and the key log lines.

  2. Run azd ai agent invoke --local "<scenario>" for each agent; it proves readiness and skill load only: toolbox calls using AgenticIdentityToken fail locally ("requires AgentInstanceClientId and AgentBlueprintClientId"). Check the backend health route, and check that the UI loads.

  3. Upsert LOCAL_FRONTEND in .env once the UI works. Leave the app running for the user (detached when available) and document how to start it.

3. Test

CheckEvidence
AcceptanceOne automated test per EARS ID: contract tests for the frozen contract, and E2E through the frontend's /api proxy (Playwright when there is a UI). Run E2E cases in parallel at c = max(1, ⌊0.8 × TPM × minutes per case ÷ tokens per case⌋), measured from one case's Responses usage and duration (TPM = capacity × 1000), and record c and its inputs in docs/verify.md. A 429 after retries is a failure.
Hostedazd ai agent invoke <agent> "<scenario>" against the deployed version. Run azd deploy <agent> first if the agent code changed. Retry 424 or 502 warm-up responses with backoff.
TelemetryA trace from this run, including backend spans, reaches Application Insights (query requests or dependencies in the last 30 minutes). Local exporters sign in as the developer (observability).
EvaluationWrite at least 10 rows from the spec scenarios to tests/eval/dataset.jsonl. Run azd ai agent eval generate --agent <svc> --dataset tests/eval/dataset.jsonl --out-file tests/eval/<svc>.eval.yaml --evaluator builtin.task_adherence --evaluator builtin.intent_resolution, adding builtin.groundedness when the agent is grounded. Then run azd ai agent eval run --agent <svc> --config tests/eval/<svc>.eval.yaml, one config per agent, check that its Agent: <name> v<N> line names that agent and the version just deployed, and keep the config with the results.
ReviewA fresh-context reviewer checks the code and evidence against each requirement, the contract and the RBAC rows, and confirms from the container app revisions that no frontend or backend revision created after the Implement gate runs a new image (re-provisioning may add same-image revisions).

4. Fix

  1. Fix the root cause in the smallest scope.

  2. Rerun the failing checks after each fix, and the full suite once after the last fix.

  3. Upsert the lesson in docs/LOOP.md (template).

Contract or scope changes go to docs/spec.md first. Never weaken a test or a threshold to get a pass.

For a failed evaluation row, read its per-row result. A judge can fail a correct refusal: when the row's stored tool calls and output match the contract, record it as an evaluator false negative with that evidence and keep the agent's behavior. Never change the agent just to raise a score.

5. Record

Write docs/verify.md with:

  • the environment and the local URLs;

  • a table mapping each requirement ID to its test, result and evidence;

  • the evaluation scores with a link to the run;

  • telemetry evidence;

  • manual test steps;

  • checks deferred to Deploy, with the reason;

  • open issues.

Gate

Pass only when:

  • every component started in this run;

  • every acceptance criterion has a passing test, a human-approved waiver listed in the workflow prompt (never self-granted) or, when only the deployed app can prove it (such as Easy Auth or public ingress), a check deferred to Deploy;

  • the report lists evidence items ({claim, source, result}, up to 12); a pass without evidence counts as failed;

  • each evaluator meets the spec's threshold (0.8 pass rate by default);

  • a trace reached Application Insights;

  • the review found no unresolved defects.

Report status, summary, artifacts, assumptions, issues and lessons.

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python, Needs Node.js

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of vieiraaevieiraae
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/foundry-agentic-loop · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Contributors
vieiraae

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download foundry-agentic-loop.zip