Cut-Point Replay: The Method That Proves Your AI Agent Bug Fix Actually Works

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Engineers Susheem Koul and Tisha Chawla presented a debugging methodology called cut-point replay at the AI Engineer World's Fair 2026, addressing a critical gap in AI agent incident response. The approach builds on recording agent runs in full detail — including prompts, model completions, tool calls, and retrieved data — to create a frozen snapshot of a failure. Rather than re-running the agent to verify a fix, which introduces fresh randomness and proves nothing, cut-point replay injects new code at a specific boundary while keeping all upstream inputs identical to the original incident. This allows developers to test whether their change produces the correct behavior given exactly the same conditions that caused the failure, without incurring API costs or non-deterministic results. The technique is designed to give engineering teams a reproducible, evidence-based way to validate fixes for production failures in AI agent systems.

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