AkasicMEM: Governed Enterprise Memory for Agents
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Abstract (arXiv)
Agent memory enables enterprise agents to retain knowledge acquired during work and reuse it across tasks and agents, turning execution experience into persistent organizational knowledge. Realizing this potential requires both source--memory integration, through which enterprise sources and accumulated memory can be utilized together, and memory governance, through which shared memory remains subject to organizational policies throughout its lifecycle. These requirements interact when information from enterprise sources persists in memory. As this information is repeatedly derived and reused under changing principals and policies, source restrictions may be bypassed, resulting in information leakage. Preventing such leakage requires authorization continuity, under which source restrictions remain effective throughout source-to-memory and memory-to-memory derivation and reuse. Existing approaches address these concerns individually, but do not treat source--memory integration, memory governance, and authorization continuity as combined core design targets across the memory lifecycle. We define Governed Enterprise Memory as agent memory designed around this combined scope and present AkasicMEM as its realization. AkasicMEM realizes authorization continuity through transitive lineage, policy composition during memory formation, and policy re-evaluation during retrieval. It is built on GraphAI's AkasicDB, a unified vector--graph--relational database whose storage and execution substrate enables the underlying operations of these mechanisms to be jointly optimized and executed.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 22 Sep 2026.
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21 of 34 references.
References · 21
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,417
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 830
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
- 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
- 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memorycited by 400
- 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 254
- 2025MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agentscited by 238
- 2025Memory Injection Attacks on LLM Agents via Query-Only Interactioncited by 144
- 2026Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionalitycited by 72
9 more
- 2025Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Controlcited by 61
- 2026Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitationscited by 21
- 2026Governed Shared Memory for Multi-Agent LLM Systemscited by 13
- 2026MAP-Graph: Provenance-Aware Shared Memory for Multi-Agent Workflowscited by 9
- 2026Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgentscited by 5
- 2026Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agentscited by 2
- 2026AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMScited by 1
- 2026Weighted Memory Tree: Remembering What Matters for Long-Horizon LLM Agentscited by 1
- 2026Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agentscited by 1
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