Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture

2026-07-05

AI Summary

Current AI agents predominantly rely on vector-based retrieval for long-term memory, which suffers from opacity, limited auditability, and constrained expressive power. This work proposes the first fully auditable, model-agnostic, and storage-decoupled structured relational memory architecture, driven by the agent itself. The system enables transparent memory management through symbolic querying, automated concept induction, and a metadata knowledge graph—without requiring external ontologies. Implemented atop a relational database, it was evaluated on a single-user academic corpus over one year, processing 44 million tokens, 110,000 text segments, 160,000 documents, and approximately 5 million relations, thereby demonstrating strong scalability and long-term stability.

Abstract

Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based similarity search, which is opaque by construction, difficult to audit, and bounded by the theoretical limits of vector representations. We present the Memory-Orchestrated Semantic System (MOSS), an agentic memory architecture in which the agent drives retrieval over a structured relational database. MOSS is model-agnostic, storage-agnostic, and API-agnostic: it runs on any relational engine, connects to any LLM provider (or to deterministic non-LLM processes), and deploys on any infrastructure, local or cloud. Its retrieval execution is symbolic and reproducible (once a query is formulated, no LLM participates in the retrieval loop) and every step of the system, from indexing to answer formulation, is logged and inspectable, making MOSS auditable by construction. Rather than imposing an external ontology, MOSS derives its conceptual vocabulary from the corpus itself. We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar's working corpus--a conversational corpus reaching back to October 2024 (some 44 million tokens, retroactively indexed) comprising 110,183 segments, alongside 163,494 catalogued documents, 569 inductively derived concepts, 322,662 concept annotations, and eleven metadata graphs totaling approximately five million relations--across four successive infrastructure generations. While the present case is that of a single researcher, the architecture is in no way specific to one person: it serves a team, an institution, or any entity that accumulates knowledge over time. We argue that auditable, sovereign, structurally unbounded memory is a precondition for AI agents intended to accompany a person or an organization over years rather than sessions.

Problem

• long-term memory
• retrieval-augmented generation
• auditable AI
• agentic memory
• relational memory architecture

Innovation

• auditable memory
• relational memory architecture
• symbolic retrieval
• agentic memory
• ontology-free semantics