MemArchitect: A Policy Driven Memory Governance Layer

TL;DR

MemArchitect introduces a policy-driven memory management layer for LLM agents, addressing governance gaps by enforcing rules for privacy, decay, and conflict resolution, thereby improving reliability and safety.

Contribution

It proposes MemArchitect, a novel governance layer that decouples memory management from model weights and enforces explicit policies for better control.

Findings

Governed memory outperforms unmanaged memory in agentic tasks

Structured memory governance enhances reliability and safety

MemArchitect effectively manages privacy and conflict resolution

Abstract

Persistent Large Language Model (LLM) agents expose a critical governance gap in memory management. Standard Retrieval-Augmented Generation (RAG) frameworks treat memory as passive storage, lacking mechanisms to resolve contradictions, enforce privacy, or prevent outdated information ("zombie memories") from contaminating the context window. We introduce MemArchitect, a governance layer that decouples memory lifecycle management from model weights. MemArchitect enforces explicit, rule-based policies, including memory decay, conflict resolution, and privacy controls. We demonstrate that governed memory consistently outperforms unmanaged memory in agentic settings, highlighting the necessity of structured memory governance for reliable and safe autonomous systems.