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Agents Optimize

AWSOfficialCreated by aws-agentcore

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • No issues found

From the author

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.
— the description by aws-agentcore

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The agents-optimize skill provides a structured approach to monitoring and refining AgentCore agents. It guides developers through setting up evaluators, configuring continuous monitoring, implementing CI/CD quality gates, and analyzing agent costs. The skill leverages project context to determine whether to focus on evaluation workflows, observability setups like CloudWatch and X-Ray, or cost optimization strategies.

What it does

  • Configures evaluators for assessing agent responses
  • Sets up continuous online monitoring for live traffic
  • Generates CI/CD quality gate scripts to validate agent performance
  • Provides guidance on setting up CloudWatch dashboards and X-Ray tracing
  • Analyzes cost drivers for AgentCore runtimes and memory usage
  • Facilitates local evaluation loops using dev traces

When to use it

  • When you need to verify if your agent is providing accurate responses
  • When you want to implement automated quality checks in your deployment pipeline
  • When you need to gain visibility into agent behavior via logs and metrics
  • When you need to understand and control the costs associated with your agent

Advantages

  • Supports local iteration on evaluators without requiring full deployments
  • Integrates with standard AWS observability tools like CloudWatch
  • Provides clear paths for both development-time and production-time monitoring
  • Helps identify specific cost drivers within the AgentCore architecture

Usage examples

“evaluate my agent”
The skill initiates an interactive session to determine if you want to run a one-time evaluation, set up monitoring, or add a CI/CD quality gate.
“set up observability”
The skill loads the observability reference and guides you through configuring CloudWatch dashboards and X-Ray tracing for your agent.
“how much does my agent cost”
The skill provides a breakdown of cost components such as runtime compute, memory events, and model usage to help you optimize your spending.
“quality gate”
The skill generates a script to integrate a quality gate into your CI/CD pipeline, ensuring builds fail if performance thresholds are not met.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/agents-optimize/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip agents-optimize.zip -d .agents/skills

Documentation by aws-agentcore

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 intentAction
Measure quality, add evaluator, run eval, CI/CD gate, online monitoringLoad references/evals.md and follow its workflow
Set up observability, CloudWatch, X-Ray, logs, metrics, dashboardsLoad references/observability.md and follow its workflow
Understand or reduce AgentCore costsLoad 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

Files

LICENSE


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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by
aws-agentcore
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · plugins/aws-agents/skills/agents-optimize · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download agents-optimize.zip