AI Agents
The Practical Guide to Building and Deploying Autonomous Systems
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AI agents are no longer theoretical. They're running in production-and they're failing. The gap between the promise of autonomous AI systems and the reality of deploying them reliably has never been wider. Engineering teams are being asked to build agents that can reason, plan, and act autonomously, but most frameworks and tutorials stop at "hello world" examples that break the moment they encounter real-world complexity. This book bridges that gap. Written for CTOs, engineering leaders, and AI practitioners who need to move beyond demos and into production, AI Agents provides the practical ar...
AI agents are no longer theoretical. They're running in production-and they're failing. The gap between the promise of autonomous AI systems and the reality of deploying them reliably has never been wider. Engineering teams are being asked to build agents that can reason, plan, and act autonomously, but most frameworks and tutorials stop at "hello world" examples that break the moment they encounter real-world complexity. This book bridges that gap. Written for CTOs, engineering leaders, and AI practitioners who need to move beyond demos and into production, AI Agents provides the practical architectures, battle-tested patterns, and hard-won lessons that separate successful deployments from expensive failures. Inside, you'll learn: ¿How to design agent architectures that are reliable, observable, and controllable ¿The memory systems, planning mechanisms, and tool integration patterns that actually work at scale ¿Why most AI agent projects fail-and how to avoid the same mistakes ¿Real-world case studies from software development, customer support, finance, and marketing ¿How to measure ROI, establish governance, and scale agents across your organization This is not a book about what AI agents might do someday. It's a manual for building them today. Whether you're deploying your first agent or scaling a fleet of them, this guide will help you navigate the complexities of autonomous systems with confidence, clarity, and a healthy respect for what can go wrong.
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