Why AI Agents Get Stuck in Infinite Loops and How to Stop Them

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AI agents running inside unconstrained loops can fail in at least four distinct ways, including oscillation, optimistic retrying, goal drift, and ambiguous completion criteria. Most of these failure modes cannot be resolved through prompting alone, as they stem from architectural limitations like finite context windows and a model's inability to recognise its own repetitive patterns. Engineers are advised to implement multiple independent stopping conditions in code, covering step counts, token usage, cost, wall-clock time, and stall detection. Each condition targets a different class of failure, meaning no single guard is sufficient on its own. A structured 'finish' tool call is the only condition that signals genuine task success, while all other limits simply indicate the run was forcibly stopped.

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ProgrammingDEV Community ·

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