How to Debug AI Agents with Traces and Evals
The article discusses the importance of debugging AI agents through a systematic approach rather than simply editing prompts. It emphasizes the need to capture traces of agent performance to identify and label failures before making changes. This method aims to improve the overall quality of AI agents by establishing a trace-to-eval loop.
- ▪AI agents often fail without clear explanations in chat transcripts.
- ▪A better debugging workflow involves capturing traces and labeling errors before modifying prompts.
- ▪OpenAI's Agents SDK provides tools for tracing LLM generations and other events during agent runs.
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Member-only storyHow to Debug AI Agents with Traces and EvalsYour AI agent failed, but the chat transcript doesn’t explain why.Sukhpinder Singh8 min read·Just now--ListenSharePress enter or click to view image in full sizeThis image was created using an AI image generation program.So someone edits the prompt, reruns one example, and calls it fixed.That is how agent quality turns into guesswork.A better workflow is slower at first and faster later: capture traces, label what actually went wrong, convert those labels into evals, and only then change the prompt, tools, routing, guardrails, or harness.
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