Why AI Agents Get Stuck Repeating or Reversing Actions Instead of Finishing

AI agents operating on multi-step tasks often lack two critical built-in capabilities: knowing when a task is truly complete and recognizing when a current approach has stopped working. This structural gap leads to two common failure modes — retry loops, where an agent repeats the same failed action expecting a different result, and oscillation, where it alternates between states and undoes its own progress. Retry loops stem from misread failures, absent failure memory, and overconfidence in the original plan, while oscillation arises from conflicting objectives, lack of persistent work memory, or inconsistent tool feedback. Oscillation is particularly difficult to detect because no single action repeats — only the broader pattern across multiple steps reveals the problem. Experts suggest that explicitly tracking progress and designing clear exit conditions into agent loops are more effective solutions than simply imposing a blunt turn or timeout limit.