arXiv:2606.24535v1 Announce Type: new Abstract: Multi-agent LLM environments require robust mechanisms for shared knowledge management. This paper formalizes the fleet-memory problem and identifies four foundational failure modes: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse. To address these, we define explicit systems-level primitives: scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation. These primitives are implemented in MemClaw, a production multi-tenant memory service, and evaluated via ArgusF
The proliferation of multi-agent Large Language Model (LLM) systems highlights the urgent need for robust knowledge management solutions, as current architectures struggle with coordination and data integrity.
Advanced memory management for multi-agent LLMs is critical for their reliability, safety, and scalability, directly impacting their ability to handle complex tasks autonomously. This directly impacts the scalability to enterprise.
The formalization of the 'fleet-memory problem' and introduction of 'governed shared memory' primitives provides a new architectural paradigm for developing more coherent and trustworthy multi-agent AI systems.
- · AI Agent development platforms
- · Enterprises deploying multi-agent systems
- · Cloud service providers offering sophisticated memory solutions
- · Ungoverned, ad-hoc memory solutions
- · Systems developers not prioritizing robust knowledge management
Improved reliability and performance of multi-agent LLM systems, leading to broader adoption in complex applications.
Increased trust in autonomous AI agents, accelerating their integration into sensitive and mission-critical workflows.
Enhanced collaboration capabilities among AI agents could unlock new levels of problem-solving previously unattainable by single models.
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Read at arXiv cs.AI