microsoft-foundry/foundry-agent/agent-optimizer/agent-optimizer.md

Agent Optimizer in Foundry — Scaffold Python Agent

Prepare an existing Python hosted agent for Agent Optimizer in Foundry, then run optimization, apply the selected candidate locally, and deploy through azd after review.

When to Use This Skill

USE FOR: make my Python agent optimizable with Agent Optimizer in Foundry, scaffold optimizer config, add load_config, prepare .agent_configs, configure eval.yaml, run azd ai agent optimize, apply optimizer candidate, deploy optimized agent.

DO NOT USE FOR: non-Python agents, prompt agents, running standalone batch evaluations, prompt optimization of an already deployed agent, or general Foundry deployment. For normal deployment, use deploy. For eval analysis loops, use observe.

Quick Reference

Property	Value
Phase	Scaffold, optimize, apply locally, deploy
Supported language	Python
Required runtime	azd project with hosted agent
Required package	azure-ai-agentserver-optimization
Required import	from azure.ai.agentserver.optimization import load_config
Required baseline	.agent_configs/baseline/ in the agent's service source directory
Supported targets	instruction, model, skill folder, function tool definitions
azd setup	azd Setup
Detailed scaffold steps	Scaffold Workflow
Python/file patterns	Python Patterns
Eval config	eval.yaml Guidance
Optimize flow	Optimize Workflow

High-Level Lifecycle

1. Prepare azd: Verify azd, login, and azure.ai.agents extension with azd Setup.
2. Scaffold: Follow Scaffold Workflow when SDK wiring or .agent_configs/baseline/ is missing; stop for review if files changed.
3. Configure eval: Create or update eval.yaml using eval.yaml Guidance.
4. Optimize: Run and monitor azd ai agent optimize with Optimize Workflow.
5. Apply and deploy: Apply the selected candidate locally, review the diff, then deploy with azd deploy.

Workflow

1. Resolve the target agent root and confirm it is a Python hosted agent.
2. Read azd Setup, then Scaffold Workflow if scaffolding is needed.
3. Read eval.yaml Guidance and configure optimization inputs from known dataset/evaluator context.
4. Read Optimize Workflow, run optimization, and ask before applying a candidate.
5. After local review and approval, deploy with azd deploy, then invoke via invoke.

Guardrails

• Target hosted Python agents only.
• Preserve existing frameworks, tools, hosting adapters, protocols, and entrypoints.
• Do not use one global scaffold across multi-agent roles unless the architecture already has one global prompt/model or the user approves.
• Keep edits scoped to the selected agent root.
• Do not apply candidates or deploy automatically; stop for review first.
• Prefer azd ai agent optimize apply --candidate plus azd deploy over direct optimize deploy so source changes are reviewable.