Not theory. Things that broke on me running real workflows.
Context bleed. Agent carries memory from a previous task into the next one. Outputs start drifting. By step 6 of 10, it's confidently wrong in ways that are hard to catch.
Confident wrong answers. Agents don't say "I don't know." They fill gaps. In outreach automation this means sometimes writing a personalised message that references something that doesn't exist. The model just invented a plausible detail.
This is the one that costs the most with clients.
The human review queue nobody designed for. You build 90% autonomous. The 10% that needs review piles up silently. Two days later, 47 things are waiting and the whole pipeline is stalled.
The workflow needed a notification system before it needed the AI.
None of these are model problems. They're systems problems.
The AI part is usually the least broken part of an AI agent.
What failures have you seen that aren't on this list?
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