Deterministic Replay for AI Agent Systems

Researchers have released agrepl, a CLI framework for deterministic replay of AI agent executions, achieving perfect replay fidelity (F = 1.0) and a median per-step latency reduction of 98.3% across five workloads. The tool intercepts external interactions via a MITM proxy, serializes execution traces, and replays them in an isolated environment with zero outbound network access, addressing the inherent non-determinism of LLM-based agent systems.

arXiv:2607.16200v1 Announce Type: new Abstract: AI agent systems that couple large language models LLMs with external tools and APIs are inherently non-deterministic: LLM sampling variance, external API state, CDN infrastructure headers, and execution-environment noise collectively prevent any prior agent run from being faithfully re-executed. Existing observability platforms capture execution logs but cannot reproduce a run in isolation. We present agrepl, a developer-first CLI framework for deterministic replay of agent executions. agrepl intercepts all external interactions at the transport layer via a man-in-the-middle MITM proxy, serialises them as structured execution traces, and replays them in a strictly isolated environment with zero outbound network access. We formalise the agent execution model, define the request-key matching function K s , and prove the determinism invariant. We introduce a noise-aware diff algorithm classifying HTTP header divergence into signal and noise tiers. Empirical evaluation across five workloads n = 250 replay instances demonstrates replay fidelity F = 1.0 and a median per-step latency reduction of 98.3%. agrepl is implemented in Go, ships as a single static binary, and is released under the MIT licence. Keywords: AI agents, deterministic replay, LLM debugging, reproducibility, MITM proxy, execution tracing, record/replay systems.