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AgentGuard, an instruction-level guardrail framework that learns conditional execution constraints from anomalous trajectories of coding agents, is presented, demonstrating that execution guardrails learned from historical failures can substantially improve the reliability of AI coding agents while highlighting the remaining challenge of balancing safety and task completion.
