Stop reading boring textbooks. Start building AI agents that actually do things.
You've heard the hype about AI agents. They book flights, debug code, research competitors, and manage workflows — all without human hand-holding. But most resources on building them are either surface-level tutorials or dense academic papers.
This book is different.
Brain-Friendly AI Agents uses a visual, hands-on learning approach inspired by cognitive science. Instead of walls of text, you get diagrams, code you can run, "Fireside Chats" between concepts, thinking exercises, and real projects — all designed to work with your brain, not against it.
You'll go from zero to production-ready multi-agent systems using Google's Agent Development Kit (ADK).
What's Inside:
Chapter 1 – Meet Your First Agent: What agents actually are, the Perceive-Think-Act-Observe loop, and why chatbots are not agents.
Chapter 2 – Getting Your Hands Dirty: Install ADK, get your API key, and build a working agent in 15 minutes.
Chapter 3 – Tools of the Trade: Give agents superpowers with FunctionTools, Google Search, code execution, and custom tools. Learn why docstrings are the most important thing you'll write.
Chapter 4 – Teaching Your Agent to Remember: Sessions, state management, and the dual-layer memory architecture (short-term scratchpad vs. long-term filing cabinet).
Chapter 5 – The Agent Assembly Line: SequentialAgent, ParallelAgent, and LoopAgent — orchestrate complex workflows like factory pipelines.
Chapter 6 – Building Your Agent Team: Multi-agent hierarchies, delegation patterns, router/specialist/coordinator architectures, and a full customer support system.
Chapter 7 – Quality Control: Callbacks, guardrails, content moderation, rate limiting — how to keep your agents safe and reliable.
Chapter 8 – Connecting to the World: MCP protocol, Google Search grounding, Vertex AI integration, and third-party tool ecosystems.
Chapter 9 – Going Live: Deploy to Google Cloud Run and Agent Engine. Production checklists, monitoring, and database-backed sessions.
Chapter 10 – What's Next?: Streaming agents, YAML configuration, cost optimization, testing strategies, and a framework comparison (ADK vs. LangChain vs. CrewAI vs. AutoGen).
This Book Is For You If:
- You're a developer who learns by doing, not just reading
- You want to build AI agents but don't know where to start
- You know Python but are new to LLMs and agent frameworks
- You prefer visual explanations over academic papers
- You want production-ready skills, not just toy examples
What You Need:
- Basic Python knowledge (functions, classes, type hints)
- A Google account (free API tier is enough to follow along)
- No machine learning experience required
135 pages. 10 chapters. Dozens of runnable code examples. Visual diagrams on every topic. One complete multi-agent system built from scratch.
Your brain will thank you.
