AI agents are everywhere, or at least everyone claims they are. But building an agent that reliably uses external tools, APIs, and databases to complete real tasks is a fundamentally different engineering challenge than writing prompts or fine-tuning models. This book bridges the gap between hype and reality. It walks you through five production-grade frameworks (DSPy, Pydantic AI, Claude Agent SDK, OpenAI Agents SDK, Google ADK), showing how each one approaches tool use, orchestration, and safety with runnable code examples, architectural comparisons, and hard-won lessons from teams deploying agents at scale.
Forward-Looking Disclaimer: This book was written with the agent framework landscape as it exists in mid-2026. Model versions, pricing tiers, API surfaces, and feature availability change rapidly. Where this manuscript references specific model names (for example, “GPT-5” or “Claude Sonnet 4”), these are illustrative projections based on publicly announced roadmaps and should be treated as such. All framework documentation URLs and code examples have been verified against live sources at the time of writing, but API surfaces may evolve. The engineering principles, patterns, and trade-off analyses presented here remain valid regardless of which specific model versions or framework releases you are using.
On “War Stories” and Illustrative Scenarios: Throughout this book, you will encounter anecdotes framed as consulting experiences. These are composite scenarios built from documented production issues, community forums, and engineering postmortems across the agent ecosystem. They are intended for pedagogical illustration to demonstrate real failure modes and debugging patterns rather than as specific verifiable case studies of named organizations. The underlying technical lessons, however, reflect genuine production challenges that teams face when deploying agents at scale.
