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Agentic AI for Serious Engineers

Agentic AI for Serious Engineers

A practical field guide to building reliable, evaluable, and production-grade agent systems

Most agentic AI material teaches you how to make an impressive demo. This book teaches engineers how to build agent systems that survive real-world constraints.

This is written for backend engineers, platform engineers, and staff+ engineers who understand distributed systems, reliability, and the cost of complexity -- but are relatively new to AI. You do not need a machine learning background. You need the judgment to know when AI helps and when it makes things worse.

What makes this different is the engineering-first approach. Every concept comes with failure modes, tradeoffs against alternatives, and evaluation criteria. There is no vibes-based testing, no "just prompt it better" advice, and no framework worship. The goal is transferable understanding: principles that hold regardless of which SDK ships next month.

Chapters

# Chapter What you learn
1 What "Agentic" Actually Means Precise vocabulary: LLM app vs workflow vs agent vs multi-agent
2 Tools, Context, and the Agent Loop Building blocks: tool registry, context engineering, observe-think-act
3 Workflow-First, Agent-Second The most important architectural decision
4 Multi-Agent Systems Without Theater When multiple agents help and when they are complexity theater
5 Human-in-the-Loop as Architecture Approval gates, escalation, and auditability
6 Evaluating and Hardening Agent Systems Eval harnesses, tracing, reliability, cost, security
7 When Not to Use Agents The signature chapter -- judgment over hype

Learning Paths

Path Chapters Time
Fast Engineer 1, 2, 7 ~1 hour
Full Mastery 1 through 7 in order ~4 hours
Enterprise Architect 1, 3, 5, 6, 7 ~2.5 hours

Projects

Two end-to-end systems built incrementally through the chapters:


This book is free to read online. Licensed under CC BY-NC-SA 4.0 -- share and adapt with credit, non-commercial use only.

GitHub Repository | sunilprakash.com