“Build an AI Agent (From Scratch)” is a fascinating step-by-step journey through design, development, and deployment of a system of autonomous AI agents. Authors Jungjun Hur and Younghee Song guide you concept by concept as you build a sophisticated research agent in Python, designed to solve complex, multi-step tasks from the rigorous GAIA (General AI Assistants) benchmark. By seeing the construction of the system end to end, you’ll understand how the underlying mechanics are unobstructed by any existing blackbox libraries and frameworks.

Early on, you’ll establish the foundational blueprint for your agent by distinguishing rigid developer-defined workflows from true, LLM-directed agent loops. You’ll learn how to manage stateless APIs, enforce structured outputs using Pydantic, and implement dynamic tool-calling. Crucially, the book centers on “Context Engineering”—the discipline of systematically structuring information to prevent context rot and the “Lost in the Middle” effect. After you build the basic agent, you’ll progressively expand its capabilities by integrating custom web search, local file system exploration, and vector-based RAG to navigate complex data. Then, you’ll explore the Code-Act paradigm, empowering the agent with sandboxed cloud environments to securely write scripts, run CLI commands, and compose tools dynamically. By building every layer from the ground up, the book ensures that listeners are left with a deep, practical understanding of AI mechanics.

About the listener: For Python developers with some knowledge of machine learning who want to build and deeply understand agentic systems without relying on abstract libraries.

About the authors: Jungjun Hur is an independent AI and data engineer who specializes in building agentic systems for e-commerce. He is the author of “Practical AI Application Development Using LLMs.” Younghee Song is an AI consultant at PricewaterhouseCoopers (PwC). Both authors live and work in South Korea.

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