agents-optimize | SkillBundle

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-started to 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.

Cross-references

After setting up evals, suggest agents-harden for production readiness
If eval results reveal agent issues, suggest agents-debug for root cause analysis
If the developer needs to add capabilities first, suggest agents-build

Output

Depends on the workflow — see the loaded reference for specific outputs.

Quality criteria

Evaluator configuration uses only valid CLI flags
Online eval sampling rate is appropriate (not 100% in production without discussion)
CI/CD quality gate has a clear pass/fail threshold
Observability setup includes both tracing and logging
The developer understands the eval data delay: ~10 seconds put-to-get, end-to-end — one ingestion step covers both trace reads and eval queries; there is no separate indexing wait

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-started to 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.

Cross-references

After setting up evals, suggest agents-harden for production readiness
If eval results reveal agent issues, suggest agents-debug for root cause analysis
If the developer needs to add capabilities first, suggest agents-build

Output

Depends on the workflow — see the loaded reference for specific outputs.

Quality criteria

Evaluator configuration uses only valid CLI flags
Online eval sampling rate is appropriate (not 100% in production without discussion)
CI/CD quality gate has a clear pass/fail threshold
Observability setup includes both tracing and logging
The developer understands the eval data delay: ~10 seconds put-to-get, end-to-end — one ingestion step covers both trace reads and eval queries; there is no separate indexing wait