Deploying Scalable Agents with Microsoft Foundry

Companion skill for [Lesson 16 – Deploying Scalable Agents](../../../16-deploying-scalable-agents/README.md).

Use it to help a learner move an agent from prototype to a scalable, observable

production deployment. Ground every recommendation in the lesson content and

the runnable notebook; do not invent Foundry APIs.

Triggers

Activate this skill when a learner wants to:

• Deploy an agent to Microsoft Foundry as a hosted agent and make it 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 an evaluation gate so a bad agent version cannot ship.
• Add a human-in-the-loop approval step for high-risk actions.
• Instrument an agent with OpenTelemetry tracing for production observability.
• Smoke-test a deployed agent as a fast post-deploy gate.

Core mental model

A production agent is mostly the operational skeleton around the model (~80%),

not the model itself. Map every recommendation to one of these concerns:

Deployment patterns (pick one, or combine)

1. Client-hosted — the reasoning loop runs in your process. Max control; you own scaling/state.
2. Hosted agent (Foundry Agent Service) — Foundry hosts the loop, stores threads, enforces RBAC/content safety, shows the agent in the portal. Less control, far less operational surface.
3. Agent workflow — multiple agents/tools composed into a graph with branching, approval nodes, and durable checkpoints.

Lifecycle (the loop that ships an agent)

create → version → evaluate (gate) → deploy hosted → observe online → collect failures → repeat.

Offline evaluation is a gate, not an afterthought — a version does not ship

unless it clears the threshold. Online observability feeds real failures back

into the offline test set.

Scaling and cost levers (in priority order)

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

Key patterns to reproduce

Point the learner at these from the notebook

[16-python-agent-framework.ipynb](../../../16-deploying-scalable-agents/code_samples/16-python-agent-framework.ipynb):

• Request handler: cache → route by complexity → trace span → run → cache.
• Evaluation gate: score an 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 in tracer.start_as_current_span(...) and set attributes like routed.model, customer.id.

Smoke-testing a deployed agent

After deploy, verify the endpoint actually answers (a green deploy can still be

silent). Use the AI Smoke Test

action via [.github/workflows/smoke-test.yml](../../../../../.github/workflows/smoke-test.yml)

with the catalog in [tests/](../../../tests/README.md). The runner POSTs each

prompt to POST {project_endpoint}/agents/{agent_name}/endpoint/protocols/openai/responses

and asserts on the reply text. The identity needs the Azure AI User role at

Foundry project scope; the token audience must be https://ai.azure.com/.

Layer the gates: smoke test (reachable/responding, every deploy) → **offline

evaluation (good enough to ship, before promotion) → online evaluation** (how

is it doing in the wild, continuous).

Enterprise controls

• RBAC: give each hosted agent a managed identity with least privilege.
• MCP in production: treat every MCP server as an untrusted boundary — pin the version, scope its identity, validate outputs, rate-limit, never expose secrets.

Guardrails for the assistant

• Prefer the canonical FoundryChatClient(...) + provider.as_agent(...) pattern used across the course.
• Do not promise live-Azure results you have not verified; recommend the smoke-test workflow to confirm a deployment.
• Keep evaluation and cost advice tied together: evaluation sets the quality floor, routing/caching keep cost near that floor.

<!-- CO-OP TRANSLATOR DISCLAIMER START -->

Disclaimer:

This document has been translated using AI translation service Co-op Translator. While we strive for accuracy, please be aware that automated translations may contain errors or inaccuracies. The original document in its native language should be considered the authoritative source. For critical information, professional human translation is recommended. We are not liable for any misunderstandings or misinterpretations arising from the use of this translation.

<!-- CO-OP TRANSLATOR DISCLAIMER END -->