deploying-scalable-agents

Deploying Scalable Agents with Microsoft Foundry

Companion skill for Lesson 16 – Deploying Scalable Agents. Use am to help learner move agent from prototype go scalable, observable production deployment. Ground every recommendation inside lesson content and the runnable notebook; no make you invent Foundry APIs.

Triggers

Activate dis skill when learner wan:

• Deploy agent to Microsoft Foundry as hosted agent and make am versioned/observable.
• Choose between client-hosted, hosted-agent, and agent-workflow deployment patterns.
• Add model routing, response caching, or bounded concurrency to control latency and cost.
• Add evaluation gate so bad agent version no fit ship.
• Add human-in-the-loop approval step for high-risk actions.
• Instrument agent wit OpenTelemetry tracing for production observability.
• Smoke-test deployed agent as quick post-deploy gate.

Core mental model

Production agent na mostly operational skeleton around di model (~80%), no be di model itself. Map every recommendation to one of dis concerns:

Deployment patterns (pick one, or combine)

1. Client-hosted — di reasoning loop dey run inside your process. Max control; you dey own scaling/state.
2. Hosted agent (Foundry Agent Service) — Foundry dey host the loop, dey store threads, dey enforce RBAC/content safety, dey show di agent for portal. Less control, far less operational wahala.
3. Agent workflow — multiple agents/tools join body as graph wit branching, approval nodes, and durable checkpoints.

Lifecycle (di loop wey dey deliver agent)

create → version → evaluate (gate) → deploy hosted → observe online → collect failures → repeat. Offline evaluation na gate, no be afterthought — version no go ship unless e clear di threshold. Online observability dey feed real failures back enter di offline test set.

Scaling and cost levers (priority order)

1. Right-size di model — use di smallest model wey fit pass di evaluation gate.
2. Route by complexity — small/fast model for simple requests, big model for real reasoning (DIY classifier or Foundry Model Router).
3. Cache — serve near-duplicate requests without model call.
4. Stateless design + bounded concurrency — externalise state; retry wit backoff.

Key patterns wey dem suppose reproduce

Point learner to these from di notebook 16-python-agent-framework.ipynb:

• Request handler: cache → route by complexity → trace span → run → cache.
• Evaluation gate: score offline test set; return pass_rate >= threshold and only deploy if true.
• Human approval: @tool(approval_mode="always_require") for actions like large refunds.
• Tracing: wrap each request inside tracer.start_as_current_span(...) and set attributes like routed.model, customer.id.

Smoke-testing deployed agent

After deploy, make sure say endpoint really dey answer (green deploy fit still dey silent). Use AI Smoke Test action via .github/workflows/smoke-test.yml with catalog for tests/. Runner dey POST each prompt to POST {project_endpoint}/agents/{agent_name}/endpoint/protocols/openai/responses and e dey check the reply text. Identity need Azure AI User role at Foundry project scope; token audience must be https://ai.azure.com/.

Combine di gates: smoke test (make sure e dey respond, every deploy) → offline evaluation (good enough to ship before promotion) → online evaluation (how e dey perform for real life, continuous).

Enterprise controls

• RBAC: give every hosted agent managed identity with least privilege.
• MCP for production: treat every MCP server as untrusted boundary — pin version, scope identity, validate outputs, rate-limit, no ever expose secrets.

Guardrails for the assistant

• Prefer di canonical FoundryChatClient(...) + provider.as_agent(...) pattern wey dem dey use for di whole course.
• No promise live-Azure results wey you never comfirm; recommend di smoke-test workflow to confirm deployment.
• Keep evaluation and cost advice together: evaluation set di quality floor, routing/caching keep cost near dat floor.

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