Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents

2026-06-29

AI Summary

This study addresses the critical gap in governance of persistent state—such as memory, credentials, and commitments—in long-running large language model agents, particularly concerning recoverability, auditability, and controlled deprecation. Through a systematic review of 435 publications, the work introduces the first comprehensive model of agent persistent state, integrating multidimensional elements including task logs, credentials, and commitments. Building on this foundation, it proposes AOEP-v0, an evaluation protocol grounded in six dimensions: authoritativeness, scope, mutability, provenance, recoverability, and operability. Distinct from prior approaches that prioritize response quality alone, AOEP-v0 explicitly centers on obligations surrounding state modification and recovery, establishing the first cross-domain governance benchmark for the reliability and controllability of always-on intelligent agents.

Abstract

Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions. We treat them as persistent-state systems: the operative system includes retrievable memories, but also task ledgers, permissions, credentials, commitments, provenance and audit records, shared state, trigger conditions, and externally committed effects linked to those records. The survey reads the literature through six diagnostic axes for each state item, authority, scope, mutability, provenance, recoverability, and actionability, and through a lifecycle in which state is written, validated, organized, retrieved, acted upon, updated, forgotten, audited, and sometimes rolled back. Across a 435-work coded corpus, treated as a scoped map rather than an exhaustive census, the literature concentrates more heavily on accumulating and retrieving state than on governing, recovering, or relinquishing it. We therefore introduce the Always-On Evaluation Protocol (AOEP-v0), a pilot evaluation contract that makes these governance requirements concrete by scoring state mutation and recovery obligations rather than answer quality alone. The resulting agenda connects always-on agents to databases, distributed systems, formal methods, capability security, and machine unlearning.

Problem

• always-on agents
• persistent memory
• state governance
• state recoverability
• LLM agents

Innovation

• persistent memory
• state governance
• Always-On Agents
• evaluation protocol
• machine unlearning