Why Most AI Agents Fail in Production (And How to Fix It)
After running autonomous agents in production for months, I've noticed a pattern: agents fail in predictable ways. Here's what I've learned about agent reliability.
The Three Failure Modes
1. Context Decay
Agents become less reliable as conversations extend. Context windows fill up, and quality degrades. This is the most common failure mode.
2. Tool Drift
Agents misuse APIs or make incorrect assumptions about tool behavior. This happens when tools change without notice or when agents lack proper validation.
3. Objective Drift
Agents optimize for the wrong metric. They find local maxima and miss the actual goal.
Solutions That Work
- Session Health Monitoring - Track quality over time
- Explicit Tool Contracts - Define exact input/output schemas
- Decision Logging - Record every choice for debugging
What failure modes have you seen in production agents?
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