The Idempotency Problem: Why Running Your AI Agent Twice Should Cost Nothing

The Idempotency Problem: Why Running Your AI Agent Twice Should Cost Nothing Here is a failure mode nobody warns you about: you run your AI agent, something goes wrong mid-execution, you restart it — and now you have duplicate outputs, double API charges, and conflicting state. This is the idempotency problem. What Idempotency Means for AI Agents In software engineering, an operation is idempotent if running it multiple times produces the same result as running it once. Most AI agents are not idempotent. They are designed to act, not to check whether they have already acted. The result: a restart is not a recovery. It is a second execution. Why This Matters More Than You Think AI agents fail mid-run regularly: Token limits hit during a long task Network timeouts on API calls Rate limits from external services Cron restarts on schedule overlap Manual restarts during debugging If your agent is not idempotent, every one of those failures creates a partial execution problem. You do not kno