Agents Optimize
Use when measuring or improving agent quality and performance, set up evaluators...
Install
npx skills add aws/agent-toolkit-for-aws --skill agents-optimize -g -a claude-code -y- Repository
- aws/agent-toolkit-for-aws3.4k
- Category
- Testing
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Lightabout 692 tokens
- Runs scripts
- No, instructions only
What it does
Use when measuring or improving agent quality and performance, set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes, use agents-debug. Slow but correct routes here; broken routes to debug.
optimize
Measure and improve your AgentCore agent's quality through evaluation, monitoring, and observability.
When to use
- You want to know if your agent is giving good answers
- You want to set up continuous quality monitoring in production
- You want to add a quality gate to your CI/CD pipeline
- You want to understand agent behavior through logs, metrics, and traces
- You want to set up CloudWatch dashboards or X-Ray tracing
Do NOT use for:
- Debugging a specific broken agent (wrong answers, errors) → use
agents-debug - Production security hardening (IAM, auth) → use
agents-harden
Input
$ARGUMENTS can be:
- An eval goal: "add a quality gate", "set up monitoring"
- An observability goal: "set up CloudWatch dashboard", "understand my traces"
- A specific evaluator: "llm-as-a-judge", "code-based"
- Empty, the skill will guide based on project context
Process
Step 0: Verify CLI version
Run agentcore --version. This skill requires v0.9.0 or later.
Step 1: Read project context
Read agentcore/agentcore.json to understand existing evaluators, online eval configs, and agent setup.
If agentcore/agentcore.json is not found:
"This skill requires an AgentCore project. Use
agents-get-startedto create one."
Step 2: Determine the workflow
| Developer intent | Action |
|---|---|
| Measure quality, add evaluator, run eval, CI/CD gate, online monitoring | Load references/evals.md and follow its workflow |
| Set up observability, CloudWatch, X-Ray, logs, metrics, dashboards | Load references/observability.md and follow its workflow |
| Understand or reduce AgentCore costs | Load references/cost.md |
| Both, "I want to understand and improve my agent" | Start with observability setup, then add evals |
Step 3: Follow the loaded reference
The reference file contains the full procedure. Follow it step by step.
