Optimize Workflow

Use this after azd setup and scaffold review are complete.

1. Prepare context

1. Resolve the hosted agent with azd Setup.
2. If SDK wiring or .agent_configs/baseline/ is missing, run Scaffold Workflow first.
3. If scaffolding changed files, stop and ask the user to review before optimization.
4. Ensure eval.yaml exists using eval.yaml Guidance, or ask whether to use built-in optimize defaults.
5. Before setting optimizer models, verify the project has deployments named GPT-5, GPT-5.1, GPT-5.2, GPT-5.4, GPT-5.5, DeepSeek-V4-Pro, or DeepSeek-V-3.2. Use only existing deployments from that allowlist.

2. Run optimize

Run from the azd project/agent root:

azd ai agent optimize

If multiple services are detected, let azd prompt or ask the user which service to use. If eval.yaml exists, use it when it matches the selected agent; otherwise ask before regenerating or ignoring it.

3. Monitor

Use these when the job is long-running or the user asks:

azd ai agent optimize status <operation-id> --watch
azd ai agent optimize list
azd ai agent optimize cancel <operation-id>

Capture the operation ID, portal URL, scores, and candidate IDs from output.

4. Apply locally

Recommend the best candidate, then ask before applying:

azd ai agent optimize apply --candidate <candidate-id>

After apply, show the source diff and summarize changed files, prompts, model/temperature, tools, and skills.

5. Deploy after review

In azd environments, prefer local apply plus:

azd deploy

Do not use azd ai agent optimize deploy --candidate <candidate-id> unless the user explicitly requests it. Local apply keeps optimized changes visible for source control review.