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

Survey of persistent memory, state, and governance in always-on LLM agents introduces AOEP-v0 evaluation protocol

A new arXiv survey paper examines 'always-on agents' — LLM-based systems whose behavior depends on durable state accumulated across interactions — through six diagnostic axes covering authority, scope, mutability, provenance, recoverability, and actionability. The authors analyze a 435-work corpus and find the literature over-indexes on state accumulation and retrieval while under-serving governance, recovery, and relinquishment. To address this gap, they introduce the Always-On Evaluation Protocol (AOEP-v0), a pilot evaluation contract that scores state mutation and recovery obligations rather than answer quality. The work connects agent design to databases, distributed systems, formal methods, capability security, and machine unlearning.