The Books · A two-volume series
Two books, one running example, built for different readers.
Both volumes follow the same project, an internal IT helpdesk assistant, from a single prompt to a multi-tenant enterprise agent. Volume I builds the mental model. Volume II builds the system.
Volumes
2 · paired
Total chapters
33 · 14 + 19
Pre-book
Free · first 50 each
Format
PDF · download
Start with Volume I if
You can read code, but AI is new to you.
- → You have heard "tool calling, RAG, agent loop" and you are not 100% sure what each is.
- → You want to build a working mental model, not memorize current tools.
- → You want one chapter, one idea, no jargon, one running example.
- → You are preparing to make architecture decisions next quarter.
Skip to Volume II if
You ship agents already and want the architecture.
- → You have a working agent in dev, and now you need cost ceilings, security review, and an on-call rotation.
- → You want gateways, routing, multi-tenant, FinOps, OWASP LLM Top 10.
- → You are preparing for a senior AI-engineering interview.
- → You want one running example carried from prompt to production platform.
Volume I · For software people new to AI
Understanding Agentic AI Systems
From GenAI to Agentic AI, Explained Simply (A Beginner's Guide)
A plain-language introduction to agentic AI for software people who are new to AI. It teaches one idea per chapter, in clear language with a real example, building a single project (an internal IT helpdesk assistant) from a plain chatbot into a real, fenced, tested agent. By the end the reader has an accurate working mental model of the whole field, not a memorized snapshot of current tools.
What you will learn
- How AI evolved through four eras, and how agentic AI differs from generative AI.
- What a language model actually is, next-word prediction, and why it both works and hallucinates.
- The three building blocks: prompting, RAG, and structured output and tool calling.
- What makes something an agent: the think, act, observe loop.
- Memory, MCP, agentic coding, and when multi-agent systems help versus just cost more.
- How to make agents safe (guardrails, human-in-the-loop) and how to evaluate them.
- A hands-on capstone: building a tiny working agent yourself.
Volume II · For engineers and architects
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
- An accurate, load-bearing mental model of how LLMs behave in production.
- Production prompting, structured outputs, function calling, and prompt registries.
- Embeddings, vector search, and the full production RAG pipeline.
- Agent design patterns, memory tiers, multi-agent tradeoffs, and bounded autonomy.
- Reference architecture for an enterprise AI system: gateways, routing, services.
- Cost, performance, and inference optimization, from token economics to caching.
- Evaluation and quality: golden datasets, LLM-as-judge, agent evals, regression.
- Reliability, security, and governance: OWASP LLM Top 10, audit, incident response.
- Voice AI, LLMOps, multi-tenant platforms, FinOps, and Forward Deployed Engineering.
- A full senior AI-engineering interview bootcamp.
Both books · One list
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