Optimize Workflow
Use this after azd setup and scaffold review are complete.
1. Prepare context
- Resolve the hosted agent with azd Setup.
- If SDK wiring or
.agent_configs/baseline/is missing, run Scaffold Workflow first. - If scaffolding changed files, stop and ask the user to review before optimization.
- Ensure
eval.yamlexists using eval.yaml Guidance, generate it withazd ai agent eval generate, or ask whether to use built-in optimize defaults. - Before setting
--optimize-modeloroptions.optimization_model, verify the project has an existing deployment from the allowed optimizer list:GPT-5,GPT-5.1,GPT-5.2,GPT-5.4,GPT-5.5,DeepSeek-V4-Pro, orDeepSeek-V-3.2.
When evaluation inputs are not already selected, generate them from a reviewed seed dataset or regenerate defaults:
azd ai agent eval generate --dataset <path-to-jsonl>
azd ai agent eval generate --reset-defaults2. Run optimize
Run from the azd project/agent root:
azd ai agent optimize --optimize-model <allowed-optimizer-model-deployment-name>If multiple services are detected, let azd prompt or ask the user which service to use. If eval.yaml exists or was generated, 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 deployDo 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.