Debugging AI agents requires a fundamentally different approach compared to traditional software. Traditional debuggers fall short when faced with non-deterministic, multi-step reasoning chains and tool usage patterns. This practical guide introduces trace-based debugging as a solution for capturing and visualizing agent decision trees.
Read moreAs AI agents become more complex, developers need better debugging strategies to understand why agents make specific decisions. This article covers five essential patterns that transform agent debugging from frustrating detective work to systematic analysis. From trace-based debugging to checkpoint replay and failure clustering.
Read moreIn the observability landscape for AI agents, developers face a critical choice: cloud-based platforms like LangSmith and Weights & Biases, or local-first tools like Peaky Peek. This comprehensive analysis explores the trade-offs between privacy, latency, cost, and debugging effectiveness. When does local-first observability make sense?
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