Most teams fail at building AI agents. They burn through API credits, chase the latest frameworks, and end up with expensive prototypes that never make it to production. The problem isn't the technology—it's the lack of a methodology. This book gives you a practical, opinionated framework for building agents that actually work.
You'll learn when to use an agent versus a simpler solution, how to design tools that models can use reliably, and how to structure loops that handle errors gracefully. This is not about prompt engineering; it is about tool design, loop architecture, and evaluation strategy.
Inside, you'll discover:
• How to decide if you really need an agent for your use case
• The agent loop: observe, plan, act, and evaluate
• Building tools that models understand and use correctly
• Memory management to prevent context collapse
• Orchestration patterns that handle 90% of use cases
• Cost and performance optimization techniques
• Debugging, evaluation, and production deployment strategies
Stop guessing and start building reliable, cost-effective agents. This book gives you the mental model you need to ship agents that create real value.
