Governed Shared Memory for Multi-Agent LLM Systems

The paper introduces a framework for governed shared memory in multi-agent LLM systems, addressing key issues such as unauthorized leakage and stale data propagation through defined primitives like scoped retrieval and provenance tracking. Implemented in MemClaw and evaluated with ArgusFleet, the system achieved 100% accuracy in provenance reconstruction and optimized write-to-visible latency to a single search round-trip, while revealing architectural challenges like asymmetric scope enforcement and pipeline ordering conflicts. This work underscores the necessity of explicit systems-level abstractions for effective multi-agent memory management in production environments, highlighting the importance of real-world evaluations to identify potential failures.