Abstract

What this paper covers

This whitepaper, Enterprise memory architecture for AI agents, is a senior technical reference for enterprise architecture, platform and risk teams working on production agentic AI deployments.

It is intended for readers who have shipped at least one pilot and now need to translate that pilot into a governed, audited, multi-team production system. We assume familiarity with enterprise integration patterns, RBAC, and basic LLM application design.

Contents

What you will get from this paper

The paper is organized into the following sections:

  • An architectural overview, with reference diagrams.
  • Component-by-component deep dives on the major design decisions.
  • Comparison with two or three alternative architectures.
  • A failure-mode catalogue with mitigation patterns.
  • A production-readiness checklist.
  • An appendix with terminology and selected references.
Who this is for

Three audiences worth calling out

The paper is written so that each audience can read selectively without losing the thread.

Enterprise architects

Design-level guidance, comparisons with alternative architectures, and patterns you can apply across multiple agent deployments.

Platform engineers

Implementation-level guidance on the components that make up a production deployment — with concrete recommendations and trade-offs.

Risk & compliance leads

Governance, audit and compliance considerations including alignment to SOC 2, ISO 27001, HIPAA, GDPR and the EU AI Act.

How to read this

A note on format

Each section is roughly self-contained, so you can read straight through or jump to the part most relevant to your role. The PDF version (linked in the CTA below) includes printable diagrams and a structured index suitable for circulation inside enterprise teams.

If you'd prefer a guided walkthrough of any of the patterns covered here, request a session with our solutions architects — we are happy to walk through specifics relative to your environment.

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