Stateless Decision Memory for Enterprise AI Agents

Key points

• There is a gap between the sophisticated stateful memory structures used in academic LLM agent research and the simpler retrieval-augmented generation (RAG) pipelines widely used in enterprise deployment.
• Enterprise AI agents in regulated domains such as finance and healthcare require core system properties such as deterministic reproducibility, auditable provenance, multi-tenant isolation, and horizontal scalability.
• Existing stateful memory structures often violate these enterprise requirements because they rely on evolving internal representations and path-dependent updates.
• The paper proposes Deterministic Projection Memory (DPM), which consists of an append-only event log and a task-conditional projection mechanism.
• The append-only event log stores raw events, such as document chunks and tool outputs, in arrival order and serves as a single persistent source of truth.
• At decision time, a stateless projection component implemented as a single temperature-zero LLM call generates a structured memory view, such as facts, reasoning, and compliance notes, from the full event log and task specification, constrained by a fixed memory budget.

Paper links

External research summaries. These are not HDATF publications or measured product results.