Building MOSAIC: Why Reproducible Replay Matters for Multi-Agent AI Experiments
The author describes MOSAIC, a research project for studying populations of language-model agents, and the difficulty of knowing whether observed behaviour changes stem from a model swap or ordinary generation variation. MOSAIC uses an append-only event log and a deterministic reducer so that a run's state can be reconstructed and checked against its recorded history. The system also records model identity via digests, generation parameters, and protocol settings, so differences between experiments can be explained. A record-and-replay mechanism saves model generations so that runs can be re-executed without fresh model calls, supporting debugging and validation. The author stresses that a system can execute exactly as designed yet still fail to yield the observations needed to answer the research question.
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