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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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Use this Skill: https://skilld.dev/gh/microsoft/skills/microsoft-foundry

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foundry-agentagent-optimizeragent-optimizer.md

≈856 tokens on demand. Your agent reads this file only when SKILL.md points to it.

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.

Source: SKILL.md on GitHub

Third-party checks

2 warnings3d4 checks · Risk SAFE
  • Gen Agent Trust Hub3d

    This skill provides a comprehensive environment for managing the end-to-end lifecycle of AI agents, models, and infrastructure on Microsoft Foundry. It includes sub-skills for deployment, evaluation, fine-tuning, and troubleshooting. The skill utilizes dynamic code execution and shell command wrappers, which are used within the context of local development and cloud orchestration. All external resources and dependencies originate from trusted organizations and well-known services.

  • Socket3d

    2 alerts: gptSecurity, gptAnomaly

  • Snyk3d

    Risk: LOW · No issues

  • Runlayer7mo

    36/36 files flagged

Provenance

Signed by skilld at 04110d9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated last week

Capability

metadata
{
  "author": "Microsoft",
  "version": "1.2.26"
}

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