Stuck in the Loop: Why AI Agents Retry, Oscillate, and Never Finish

Short summary

Most AI agent loops lack a natural exit condition, leading to two failure modes: retry loops where the agent repeats identical failed actions, and oscillation where it cycles between states undoing its own progress. Retry loops stem from misread failures and no failure memory; oscillation arises from conflicting objectives, noisy tool feedback, and lack of persistent progress tracking. The article recommends explicit progress tracking, state hashing, and distinct failure-handling strategies to catch both patterns before a blunt turn limit intervenes.

• •Retry loops occur when agents repeat identical failed actions due to misread failures and no failure memory
• •Oscillation is subtler — agents cycle between states, undoing their own progress, and is harder to detect programmatically
• •Fixes include explicit progress tracking, state hashing/diffing, and treating failure as a trigger for a new strategy, not a retry

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