**Structured Memory Snapshots: Niche Debugging Gems from Real-World Clinical Agentic Trials**
Key Takeaways AI agents often fail in ways that are invisible to traditional logs, making it impossible to understand the root cause of a critical error. This is the "black box" problem. The solution is Structured Memory Snapshots , which act like a flight data recorder, capturing the agent's complete internal state at critical moments. This technique is essential in high-stakes fields like healthcare, allowing teams to find and fix transient bugs that would otherwise be undiscoverable. An AI agent, designed to optimize patient scheduling for a Phase 3 oncology trial, almost killed someone. It wasn't a malicious act or a sci-fi robot rebellion. It was a silent, single-bit data corruption during an EHR data transfer. A patient's potassium level was misread, and the agent, following its logic perfectly, scheduled them for a high-dose infusion. A human nurse, doing a routine double-check, caught the discrepancy just hours before the appointment. The system ...
