Deterministic Replay for AI Agent Systems

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[Submitted on 30 Apr 2026]

Title:Deterministic Replay for AI Agent Systems

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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.

Comments: 9 pages, 5 figures, 5 tables

Subjects:

Artificial Intelligence (cs.AI)

MSC classes: 68N19, 68N30(Primary) 68T42, 68T01 (Secondary)

ACM classes: I.2.5; D.3.2; I.2.11

Cite as: arXiv:2607.16200 [cs.AI]

(or arXiv:2607.16200v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.16200

arXiv-issued DOI via DataCite

Submission history

From: Rasheed Mudasiru Mr [view email] [v1] Thu, 30 Apr 2026 12:00:45 UTC (1,052 KB)

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