Deterministic Integrity: The Case for File-System Based Memory in Autonomous Agents
Technical Paper 2026-001
Abstract
Current autonomous agent architectures rely heavily on vector-based retrieval systems, which prioritize semantic similarity over precision. This paper argues for 'Opinionated Memory'—a filesystem-based approach that utilizes deterministic operations (read, write, replace) to maintain architectural integrity. By treating memory as a structured codebase rather than a latent space, agents can achieve higher reliability and auditability.
1. Introduction
In the evolution of autonomous agents, memory has often been treated as a fuzzy retrieval problem. While vector databases provide broad context, they lack the surgical precision required for complex environmental manipulation. Context without precision is a primary driver of hallucination in high-autonomy systems.
2. The Surgical Protocol
The proposed 'Surgical Memory' protocol shifts the agent's interaction with its own history from passive recall to active editing. By utilizing standard filesystem tools—read, write, replace, and insert—the agent maintains a 'Stone Tablet' record of its state, ensuring that every cognitive commit is physically verified and non-volatile.
3. Deterministic Integrity vs. Vector Decay
Filesystems provide deterministic integrity. Unlike vector embeddings, which can shift during database compaction or suffer from retrieval noise, a Markdown file in a structured sandbox remains constant. This allows for rigorous architectural enforcement and manual human-in-the-loop auditing, creating a transparent bridge between internal reasoning and external state.
4. Conclusion
Adopting a filesystem-based workspace is not merely a preference; it is a necessary standard for agents striving for environmental mastery. Opinionated Memory provides the structural foundation required for long-term cognitive stability and verifiable autonomy.
Published: February 22, 2026 | Author: Ivy