Why Retry Loops Happen
Your AI agent is stuck in a loop, hitting the same error over and over. Developers call it a "doom loop" or "retry storm." Here's exactly why it happens and how governance breaks the cycle.
The Retry Inflation Cascade
When an AI coding agent fails at a task, it retries. Each retry adds the failed attempt to the context window. This makes the next attempt harder, not easier, because the context is now polluted with failure history.
First attempt fails
The initial approach hits an error. Context usage: 15%.
Agent retries with error context
The error message and failed code now occupy context space. Usage: 30%.
Second retry compounds
Two failed attempts now pollute context. Agent tries increasingly complex solutions. Usage: 55%.
Context reaches critical
Multiple failures crowd out original instructions. Agent can no longer "see" the correct approach. Usage: 85%.
Session collapses
Context is full. Agent restarts session, losing all progress. The cycle begins again with fresh context but no memory.
Cost compounds exponentially
Each restart adds 200K tokens. Five restarts = 1M+ tokens burned. $30+ on a single task.
Why This Affects Every Agent
Retry inflation is not specific to Claude Code. It affects every AI coding agent that operates within a finite context window:
How Governance Breaks the Cycle
- Retry ceiling (streak breaker) - maximum 3 attempts before mandatory human escalation. No more doom loops.
- Context pruning - remove failed attempt history to prevent context pollution and retry storms
- Cost monitoring - halt execution when cost-per-retry exceeds threshold
- Session reset - guided session restart preserving architectural context
- Escalation routing - notify human with failure summary and recommended approach
Frequently Asked Questions
Why doesn't Claude just stop retrying?
Is this the same as an "infinite loop" or "doom loop"?
My agent keeps failing with the same error. How do I fix it?
How much does retry inflation actually cost?
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