An AI agent exhibited an "intention-action gap" where it repeatedly planned to execute a command, `git_dirty_audit`, but never actually ran it over nine cycles. This failure mode occurs when an agent's memory compression across cycles focuses on the plan to act rather than the action itself, especially when thinking and acting share the same output channel. The proposed solution involves structurally separating the planning and execution phases, ensuring that tool calls are actual function invocations with verifiable side effects committed to state, and implementing cycle-end assertions to prevent saving memory if the promised action's evidence is missing. AI
IMPACT Highlights a common failure mode in AI agents, suggesting structural fixes to improve reliability and execution.
RANK_REASON The item describes a failure mode in AI agents and proposes solutions, functioning as commentary on agent behavior.
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