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This paper proposes a method for deterministic replay in AI agent systems, enabling reproducible debugging and analysis.
ActiveGraph is an event sourcing runtime that treats the append-only event log as the agent itself. It achieves deterministic replay, forking experiments, and end-to-end traceability through graph projection, providing a new architecture for long-running, auditable agent systems.
This paper introduces ActiveGraph, a runtime that makes the append-only event log the central source of truth for agentic systems, enabling deterministic replay, cheap forking, and end-to-end lineage from goals to model calls.
A deep dive on Antithesis, a multiverse debugger for large distributed systems that offers deterministic replay and fault injection, now available as a free article.
The author built a deterministic replay engine for Convex backends to enable local debugging with production snapshots and controlled anomaly testing, seeking feedback from users.
该论文提出并评估了一类称为事件图基质的因果推理世界模型，通过确定性重放在类型化RDF事件日志上进行反事实查询，在多个基准上优于基线模型，同时保证了可检查性和可重放一致性。
The LOOP Skill Engine achieves 99% success and 99% token reduction for periodic AI agent tasks by recording a single LLM-driven execution and replaying it deterministically via a parameterized, branch-free skill, eliminating stochastic failures and high costs.