Open-source tool lets AI agents fork, rewind, and replay runs

Researchers from Northeastern and Stanford released Shepherd, an MIT-licensed Python runtime that records every agent-environment interaction, including process state and filesystem together, as a Git-like, copy-on-write execution trace. The paper reports 5x faster forking than Docker, over 95% prompt-cache reuse on replay, and a live supervisor that raised CooperBench pair-coding pass rates from 28.8% to 54.7%.

Why it matters: Long agent runs currently lose recoverable state when something goes wrong: you either burn tokens patching forward or restart and re-pay every call. A cheap, general rewind primitive like this could become standard agent infrastructure, the same way checkpointing became standard in distributed training.