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 build an impressive demo. This book teaches engineers how to build agent systems that survive real-world constraints: unclear requirements, bad tool outputs, partial failures, prompt injection, and cost pressure.
This repository is the code companion. It contains working Python implementations for every concept, 52+ passing tests, two end-to-end projects, and 22 hand-crafted architecture diagrams.
New to agentic AI?¶
Start with the Foundations -- four hands-on sections that take you from zero to building your first agent. No framework required. No prior AI experience needed.
| # | Section | What you learn |
|---|---|---|
| 0a | How LLMs Actually Work | The engineer's mental model: APIs, tokens, context, hallucination |
| 0b | From API Calls to Tool Use | Function calling, schema validation, giving the model hands |
| 0c | Your First Agent, No Framework | Build a complete agent in 100 lines. See it work. See it break. |
| 0d | The Same Agent, With a Framework | ADK and LangChain side-by-side. Eval comparison. Choose with data. |
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 |
Chapter 1 is available as a free sample. The full book is on Amazon.
Projects¶
Two end-to-end systems built incrementally through the chapters:
- Document Intelligence Agent -- Ingest documents, retrieve evidence, answer with citations, escalate on uncertainty
- Incident Runbook Agent -- Inspect signals, search runbooks, propose remediation, request human approval
Evidence¶
- Baseline Eval Report -- Gold dataset evaluation with rubric scoring
- Architecture Comparison -- Workflow vs agent side-by-side metrics
- Trace Examples -- Structured execution traces with token accounting
- Failure Case Studies -- Real failure analysis and lessons learned
Get the book on Amazon | GitHub Repository | sunilprakash.com