AI Agents in Production: 6 Failure Modes and Their Fixes

AI agent demos look magical — then they hit real production data and quietly fall apart. Here are the 6 failure modes I keep seeing, and the engineering fix for each one. Every impressive agent demo rests on clean data, a short task, and the one take that worked. Production has none of that: dirty columns, hour-long tasks, partial failures, and bills that add up. In this video I walk through the failures that actually break agents — missing context, no memory between steps, silent wrongness, runaway cost loops, irreversible actions, and no evals — with a concrete story for each and the pattern that fixes it. None of these are "wait for the next model" problems. They're engineering problems you can solve today. ⏱️ Chapters 0:00 - Why agent demos die in production 2:12 - The 4 real causes (it's not the model) 2:58 - Failure 1: The missing-context trap 5:44 - Failure 2: Agents with no memory 8:10 - Failure 3: Silent wrong answers 11:17 - Failure 4: The $300 runaway loop 14:05 - Failure 5: Irreversible actions 15:46 - Failure 6: No evals, no progress 17:36 - Design rules for agents that work #AIagents #agents #LLM #LLMs #productionAI #agentfailures #AIengineering #contextengineering #agentmemory #evals #humanintheloop #AIcost #dataengineering #DataGuy