AI Agents for Beginners

Five frameworks. One company. Real code you can run. Everyone is talking about AI agents. Few can explain what one actually is, when a product needs one, or which framework to build it with. AI Agents for Beginners answers all three questions the only way that works: by building the same company's work five times, once in each of today's leading agent frameworks, on identical data and tools, so they can finally be compared side by side.

Company X is a mid-size online retailer. Across five hands-on chapters you build its:

Refund assistant in LangGraph — a checkpointed graph that pauses for a manager's approval and survives restarts Weekly competitor report in CrewAI — a three-role crew with structured output and guardrails Multi-specialist support desk in the OpenAI Agents SDK — triage, handoffs, input and output guardrails, sessions Supplier contract reviewer in Microsoft Agent Framework — a three-reviewer group chat on a .NET portal (with a C# edition) AI-native shopping assistant in Google ADK — an agent tree with a deterministic, code-guarded order flow

Every product ships with an evaluation set, a deterministic baseline, and a working repository. Every number in the book was produced by running the code.

What you will learn

The three levels every AI product sits at — AI Embedded, AI First, AI Native — and how to decide which one you are building before you write a line of code The seven building blocks every agent framework provides: model, tools, state and memory, orchestration, handoff, guardrails, observability How each framework actually works — its core metaphor, its vocabulary, its strengths, and when to look elsewhere A 12-criterion comparison and a 7-question decision tree for choosing a framework by the product and the estate, not by popularity MCP and A2A, explained plainly and used to connect all five products into one system Taking agents to production: evaluation, one OpenTelemetry dashboard for every framework, cost control, four layers of safety, and a 20-item release checklist The rules that keep an agent dependable: the model proposes, the rules decide; guard every write; bound every loop; evaluate everything

Who this book is for Developers, architects, product managers, and technical founders who want to move from experimenting with AI to shipping agent products they can trust. You need basic Python and the willingness to run code; you do not need prior agent experience or a machine-learning background.

Inside

17 chapters in three parts, each with objectives, a one-page summary, a Company X exercise, and review questions with answers Over 50 original diagrams and 100+ tested code listings Six appendices: the same task in five frameworks, the companion repository and C# edition, printable templates, MCP and A2A, a glossary, and the series

The AI-Native Series from MOBILUCK and code247.ai helps builders move from using AI as a feature to building with AI as a foundation. This is the first book.