Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Private Agent Systems: Architecture for Private, Observable, and Governed AI Agents

Private Agent Systems: Architecture for Private, Observable, and Governed AI Agents

Verì, Daniele

Paperback

ProgrammingComputer Security

ISBN13: 9798199967976
Publisher: Independently Published
Published: Jun 8 2026
Pages: 354
Weight: 1.04
Height: 0.74 Width: 6.00 Depth: 9.00
Language: English
AI agents have moved from demos into internal tools, support workflows, coding environments, knowledge systems, and private infrastructure. That shift creates a new engineering problem. A fluent answer is no longer enough. Teams need to know which data an agent may see, which model route it may use, which tools it may call, which actions require approval, which traces prove what happened, and when the system must stop.

In Private Agent Systems, Daniele Verì treats agents as production software systems, not prompt experiments. The book focuses on the harness around the model: identity, data classification, retrieval, model routing, tool permissions, memory, observability, evaluation, privacy controls, and operational ownership. It gives AI platform engineers, LLMOps engineers, solution architects, security reviewers, privacy reviewers, and technical leads a practical architecture for building agents that can handle real organizational data without relying on trust in the demo.

The book starts with a working definition of private, observable, governed agents and explains why enterprise labels from vendors are not enough on their own. It then walks through the technical stack: local, hybrid, and cloud model routes; local inference; model notation and adaptation; retrieval, GraphRAG, and multimodal evidence; observability; evals; and debugging. Later chapters turn privacy, consent, security, tool governance, deployment topology, and production rollout into runtime decisions that can be inspected and reviewed.

Two first-party case studies, Amber and Relic, ground the architecture in concrete systems. Amber shows how Hybrid GraphRAG can become governable through source lifecycle management, tenant isolation, retrieval routing, citations, evaluation, and admin observability. Relic explores runtime governance for longitudinal agents: governed memory, provenance, correction, consent, privacy traces, and evidence sufficiency. The final chapters show how agentic development workflows and production reviews can ship agent systems without losing scope, evidence, or rollback paths.

Readers will learn how to:

- Design an agent harness before real data enters the system

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