Memory Ownership Architecture: Toward Governed, Provenance-Aware Cognitive Persistence for Long-Lived Autonomous AI Agents
Year: 2026
Type: preprint
Abstract: Long-lived autonomous AI agents require persistent, trustworthy memory to maintain coherent behavior across extended operational horizons. Existing approaches — including Retrieval-Augmented Generation (RAG), hierarchical memory paging systems such as MemGPT, and simulation-oriented memory streams — treat memory as a retrieval optimization problem. They optimize for semantic similarity while neglecting ownership semantics, provenance traceability, compliance governance, and mutation accountability. This architectural gap becomes critical at enterprise scale, where regulatory requirements (GDPR, HIPAA), multi-tenancy constraints, and forensic auditability impose governance obligations that retrieval optimization alone cannot satisfy. We introduce the Memory Ownership Architecture (MOA), a five-layer framework that reconceptualizes AI agent memory as governed cognitive infrastructure. MOA introduces four core innovations: (1) Memory Contracts, typed primitives that bind each memory object to explicit ownership, mutability, visibility, retention, and portability semantics; (2) a Multi-Factor Confidence Decay model that degrades retrieval priority as memories age, accumulate contradictions, or receive user corrections; (3) Editable Memory Lineage, a Git-inspired versioned mutation tracking system that preserves the complete history of memory state; and (4) Memory Isolation Boundaries that formally prevent cross-agent cognitive contamination in multi-tenant deployments. We further introduce the Memory Governance Benchmark (MGB), a proposed evaluation framework comprising five task categories and eight metrics designed to assess governed memory systems — properties that existing agent memory benchmarks do not measure. We state five falsifiable research hypotheses and provide formal definitions for the core architectural primitives. Empirical evaluation via an AWS-native implementation is forthcoming.
Author: Debnath, Suplab
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7162441391