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agents-optimize

面向 AWS Bedrock AgentCore 项目，读取项目配置后按目标加载评测、可观测性或成本参考：设置评测器与在线采样、搭建 CloudWatch 仪表盘与 X-Ray 追踪、增加 CI/CD 质量门禁，并分析延迟与成本；不负责错误调试或安全加固。

aws
热度 0.00
最近更新 2026-10-18 16:44:40
分类：数据依赖：代码执行 / 读写文件 / 第三方 API应用场景：报告撰写 / DevOps / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add aws/agent-toolkit-for-aws/agents-optimize
Metadata
name
agents-optimize
description
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.
allowed-tools
Read Grep Glob Bash
metadata
{"type":"skill","version":"1.0.0","author":"aws-agentcore","requires-cli":">=0.9.0"}

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