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Volume II · For engineers and architects
Designing Enterprise Agentic AI Systems
An Architect's Field Guide for Engineers
For engineers and architects building agentic systems in production. Assumes a working understanding of GenAI and agents (the level Book 1 teaches). Also serves senior AI-engineering interview prep.
About the book
Designing Enterprise Agentic AI Systems
An Architect's Field Guide for Engineers
A field guide for engineers and architects designing agentic AI systems for real organizations, systems with cost ceilings, security reviews, and an on-call rotation. Across ten chapters it follows one running example, an IT helpdesk assistant, as it grows from a single prompt into a secured, evaluated, multi-tenant enterprise platform with a voice channel. Where Book 1 explains what an agent is, this book delivers the engineering judgment, architecture, and hard tradeoffs of running agents reliably at scale.
What you will learn
10 things you walk away with
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An accurate, load-bearing mental model of how LLMs behave in production.
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Production prompting, structured outputs, function calling, and prompt registries.
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Embeddings, vector search, and the full production RAG pipeline.
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Agent design patterns, memory tiers, multi-agent tradeoffs, and bounded autonomy.
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Reference architecture for an enterprise AI system: gateways, routing, services.
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Cost, performance, and inference optimization, from token economics to caching.
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Evaluation and quality: golden datasets, LLM-as-judge, agent evals, regression.
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Reliability, security, and governance: OWASP LLM Top 10, audit, incident response.
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Voice AI, LLMOps, multi-tenant platforms, FinOps, and Forward Deployed Engineering.
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A full senior AI-engineering interview bootcamp.
Table of contents
3 parts · 19 chapters
- 01 How to Read This Book
- 02 Reading Paths, and a Fast Lane
- 03 Foundations: How LLMs Actually Work
- 04 Prompt Engineering and Structured Outputs
- 05 Embeddings, Vector Search, and RAG
- 06 Agents and Agentic Systems
- 07 AI System Architecture
- 08 Cost, Performance, and Inference Optimization
- 09 Evaluation and Quality
- 10 Reliability, Security, and Governance
- 11 Voice AI, MLOps, and Enterprise
- 12 Coda: Forward Deployed Engineering
- 13 Frontiers and Interview Bootcamp
- 14 Glossary
- 15 Sources and Notes
- 16 Topic Finder
- 17 Index of Mental Models
- 18 One-Page Reference Card
- 19 Closing
Before Volume II
Understanding Agentic AI Systems
Volume I · Foundations
The senior follow-on once the basics click.
From GenAI to Agentic AI, Explained Simply (A Beginner's Guide)
Read about Volume I →Pre-book
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