Your AI Agent Is Confidently Wrong About Production — Willem Pienaar, Cleric

This post contributes to agent memory and harness engineering by detailing the challenges of using AI agents for production debugging, where feedback loops are absent and environments are unpredictable. It identifies four common failure modes: mistaking symptoms for causes, lacking baseline knowledge, abstraction loss in sub-agent summaries, and anchoring on past incidents. The talk proposes grounding techniques such as generating parallel theories, computing baselines, using dependency graphs, incorporating timing, and propagating evidence. Crucially, it emphasizes verifying fixes against real outcomes to calibrate agent confidence, thereby enabling more reliable production debugging.