"The Agent Didn't Decide Wrong. The Instructions Were Conflicting — and Nobody Noticed."

Last week I posted Agent Forensics on Reddit — an open-source black box that records every decision an AI agent makes and generates forensic reports when things go wrong. The response was great. But one comment stopped me cold: "The magic mouse example is perfect because it shows the real problem isn't that the agent 'decided wrong' — it's that the system prompt had conflicting priorities. 'Buy what the user asked for' vs 'find the best deal,' and the model resolved the ambiguity silently." And then the part that really hit: "A decision log helps you find these after the fact, but the harder problem is preventing them. The most useful insight isn't 'what went wrong' — it's 'where did the model encounter ambiguity and pick one interpretation without flagging it.'" They were right. And my tool couldn't do that yet. The Blind Spot Let me show you exactly what was missing. In v0.1, when the shopping agent bought a Logitech instead of an Apple Magic Mouse, the forensic trail showed: [DECISI