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AI Agent Engineering by Projects: Build 8 Autonomous Systems from Scratch using LLMs, LangGraph, and CrewAI for Real-World Automation
Paperback
Technology & EngineeringGeneral Computers
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Publisher: Independently Published
Published: Jan 20 2026
Pages: 172
Weight: 0.68
Height: 0.37 Width: 7.00 Depth: 10.00
Language: English
AI Agent Engineering by Projects: Build 8 Autonomous Systems from Scratch using LLMs, LangGraph, and CrewAI for Real-World Automation
What happens when your AI stops sounding smart and starts failing in production?If you have built with LLMs long enough, you have seen it. Agents that hallucinate instead of checking. Automations that loop forever. Systems that look impressive in demos but collapse under real inputs, real tools, and real constraints. The problem is not intelligence. The problem is engineering.
AI Agent Engineering by Projects is about turning fragile, chat-style AI into dependable autonomous systems. This book shows how to design agents that act, observe, recover from failure, and operate safely without constant human babysitting. At its core, it tackles the hardest question in modern AI development: how do you make agents reliable when the environment is messy and the model is imperfect?
Instead of abstract theory, you will build eight complete agent systems from scratch. Each project exposes a real failure mode, then engineers the control layers that prevent it. You learn to treat agents as software systems with contracts, loops, boundaries, and accountability.
By the end of this book, you will be able to:
Build agents that stop guessing and start using tools correctly, beginning with a no-hallucination calculator that establishes the Reason-Act-Observe loop
Design graph-based agent control flow with LangGraph, replacing brittle chains with resilient, inspectable execution paths
Create local and privacy-first agents that safely interact with files, browsers, and operating systems
Engineer retrieval-backed assistants that prove answers instead of asserting them
Orchestrate multi-agent teams with CrewAI, including role definition, task delegation, and structured collaboration
Implement self-healing loops where agents detect errors, correct themselves, and terminate safely
Add production-grade safeguards: sandboxed execution, testing, observability, and hard cost limits
Ship agents as real services, not scripts, with deployment and operational patterns that scale
This book is written for developers, engineers, and technical builders who want AI systems that hold up on bad days, not just good demos. You will come away with reusable patterns, working architectures, and a mindset that prioritizes evidence over optimism.
If you are ready to stop prompting and start engineering, Order this book and build agents that actually run.
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