Agent Diagnosis and Optimization

Reddit r/AI_Agents Tools
agent design-review debugging optimization security evaluation observability

Summary

The author shares an agent-design-review Skill for systematically diagnosing and optimizing Agent architectures, covering Prompt, tool permissions, context, security, memory, evaluation, cost, observability, and more, and outputting evidence-based P0/P1/P2 issues to help avoid common pitfalls.

The most common pitfall when building Agents is focusing all attention on the Prompt. A well-written Prompt does not mean the Agent is reliable. When it actually goes live, the more likely problems are: - Whether tool permissions are out of control - Whether context and Tokens are wasted - Whether recovery and termination are possible after failure - Whether Memory and RAG have privacy leaks - Whether evaluation only checks if the model "said it was done" - Whether multi-Agent setups only add cost and complexity So I put together an agent-design-review Skill. It can inspect the Agent's architecture, Prompt, context, tools, security, memory, evaluation, cost, observability, and multi-Agent design, and output evidence-based P0/P1/P2 issues. It does not arbitrarily judge failure due to missing materials, nor does it use a single overall score to mask serious security issues. Design documents, code implementations, and production evidence are evaluated separately. The Skill is fully standalone, with built-in Chinese and English reference materials, review templates, static scanning scripts, and portability tests, so it can be directly copied to other projects.
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