Debugging AI Agents Requires Tracing the First Bad Decision, Not the Final Output

A developer shared a key insight after spending hours trying to fix an AI agent that kept selecting the wrong tool. The root cause turned out not to be in the final response or prompt, but in a subtly incorrect retrieval that occurred eight steps earlier in the execution chain. Each subsequent decision cascaded from that single early divergence, meaning the visible error was only a symptom of a much earlier failure. This experience highlighted a fundamental difference between debugging traditional software and AI agents: instead of looking for where the system crashes, developers must identify where it first goes wrong. The author now frames debugging around the question of which decision first diverged, rather than why the final answer was incorrect.