Governed Memory: A Production Architecture for Multi-Agent Workflows

• URL: http://arxiv.org/abs/2603.17787v1
• Date: Wed, 18 Mar 2026 14:49:31 GMT
• Title: Governed Memory: A Production Architecture for Multi-Agent Workflows
• Abstract summary: Enterprise AI deploys dozens of autonomous agent nodes across, each acting on the same entities with no shared memory governance.<n>We identify five structural challenges arising from this memory governance gap.<n>We present Governed Memory, a shared memory and governance layer addressing this gap through four mechanisms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Enterprise AI deploys dozens of autonomous agent nodes across workflows, each acting on the same entities with no shared memory and no common governance. We identify five structural challenges arising from this memory governance gap: memory silos across agent workflows; governance fragmentation across teams and tools; unstructured memories unusable by downstream systems; redundant context delivery in autonomous multi-step executions; and silent quality degradation without feedback loops. We present Governed Memory, a shared memory and governance layer addressing this gap through four mechanisms: a dual memory model combining open-set atomic facts with schema-enforced typed properties; tiered governance routing with progressive context delivery; reflection-bounded retrieval with entity-scoped isolation; and a closed-loop schema lifecycle with AI-assisted authoring and automated per-property refinement. We validate each mechanism through controlled experiments (N=250, five content types): 99.6% fact recall with complementary dual-modality coverage; 92% governance routing precision; 50% token reduction from progressive delivery; zero cross-entity leakage across 500 adversarial queries; 100% adversarial governance compliance; and output quality saturation at approximately seven governed memories per entity. On the LoCoMo benchmark, the architecture achieves 74.8% overall accuracy, confirming that governance and schema enforcement impose no retrieval quality penalty. The system is in production at Personize.ai.

Related papers

• When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary [12.82709779460352]
  LLM agents adapt across tasks by consolidating heterogeneous interaction histories into reusable facts, preferences, observations, and rules.<n>We identify authority collapse, in which consolidation preserves a claim while erasing the source constraints governing its authorized use.<n>We show that memory-driven adaptation must preserve not only what was learned, but also the authority under which it may be reused.
  arXiv  Detail & Related papers  (2026-08-03T04:15:40Z)
• Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy [79.09714980186278]
  Building persistent embodied agents in unstructured environments requires unified orchestration of heterogeneous tools.<n>We present OmniAct, a framework integrating a multimodal semantic planner for skill routing across unified action spaces.<n>It achieves consistent improvements in end-to-end success across all complexity levels.
  arXiv  Detail & Related papers  (2026-06-25T16:36:35Z)
• Human-Inspired Memory Architecture for LLM Agents [0.9507070656654629]
  We present a biologically-grounded memory architecture comprising six cognitive mechanisms.<n>Each mechanism addresses a specific failure mode of naive memory accumulation.<n>At S-tier scale (50 sessions), dedup-based consolidation yields a +13.3 pp improvement in preference recall.
  arXiv  Detail & Related papers  (2026-05-08T22:52:37Z)
• Effect-Transparent Governance for AI Workflow Architectures: Semantic Preservation, Expressive Minimality, and Decidability Boundaries [0.0]
  We present a machine-checked formalization of structurally governed AI workflow architectures.<n>We prove that effect-level governance can be imposed without reducing internal computational expressivity.
  arXiv  Detail & Related papers  (2026-05-01T18:52:47Z)
• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers [0.42061757959666934]
  Large language model (LLM) agents increasingly operate in settings where a single context window is too small to capture what has happened.<n>Memory is what turns a stateless text generator into a genuinely adaptive agent.<n>This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents.
  arXiv  Detail & Related papers  (2026-03-08T15:08:01Z)
• MAPLE: A Sub-Agent Architecture for Memory, Learning, and Personalization in Agentic AI Systems [0.0]
  Large language model (LLM) agents have emerged as powerful tools for complex tasks, yet their ability to adapt to individual users remains fundamentally limited.<n>We argue this limitation stems from a critical architectural conflation: current systems treat memory, learning, and personalization as a unified capability rather than three distinct mechanisms.<n>We propose MAPLE, a principled decomposition where Memory handles storage and retrieval infrastructure; Learning extracts intelligence from accumulated interactions asynchronously; and Personalization applies learned knowledge in real-time within finite context budgets.
  arXiv  Detail & Related papers  (2026-02-03T03:46:39Z)
• E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory [4.8183840404266185]
  E-mem is a framework shifting from Memory Preprocessing to Episodic Context Reconstruction.<n>E-mem achieves over 54% F1, surpassing the state-of-the-art GAM by 7.75%, while reducing token cost by over 70%.
  arXiv  Detail & Related papers  (2026-01-29T13:42:42Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs [62.116710797795314]
  Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance.<n>We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memory based on task context.
  arXiv  Detail & Related papers  (2025-11-18T21:51:23Z)
• SEDM: Scalable Self-Evolving Distributed Memory for Agents [23.182291416527764]
  SEDM is a verifiable and adaptive framework that transforms memory from a passive repository into an active, self-optimizing component.<n>We show that SEDM improves reasoning accuracy while reducing token overhead compared with strong memory baselines.<n>Results highlight SEDM as a scalable and sustainable memory mechanism for open-ended multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-09-11T14:37:37Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving [62.71545696485824]
  We introduce AGENT KB, a universal memory infrastructure enabling seamless experience sharing across heterogeneous agent frameworks without retraining.<n>AGENT KB aggregates trajectories into a structured knowledge base and serves lightweight APIs.<n>We validate AGENT across major frameworks on GAIA, Humanity's Last Exam, GPQA, and SWE-bench.
  arXiv  Detail & Related papers  (2025-07-08T17:59:22Z)
• AutoLayout: Closed-Loop Layout Synthesis via Slow-Fast Collaborative Reasoning [102.71841660031065]
  Auto is a fully automated method that integrates a closed-loop self-validation process within a dual-system framework.<n>The effectiveness of Auto was validated across 8 distinct scenarios, where it demonstrated a significant 10.1% improvement over SOTA methods.
  arXiv  Detail & Related papers  (2025-07-06T08:35:22Z)
• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory [0.5584627289325719]
  Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses.<n>But their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues.<n>We introduce Mem0, a scalable memory-centric architecture that addresses this issue by dynamically extracting, consolidating, and retrieving salient information from ongoing conversations.
  arXiv  Detail & Related papers  (2025-04-28T01:46:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.