Build an AI Agent (from Scratch)
- Use MCP to integrate tools calls into your agent's workflow
- Agentic RAG for relevant responses
- Create memory modules that store facts, context, and evolving goals
- Enable agents to plan, reflect, and self-correct
- Build specialized agents, including a code execution agent
- Design multi-agent systems About the book Build an AI Agent (From Scratch) is a step-by-step guide to creating a working AI agent, starting with the bare essentials and growing your AI into a full-featured, real-world system. You will connect your agent to powerful software tools, implement a reasoning loop, and then extend your agent with retrieval, memory, planning, reflection, and even multi-agent coordination. Along the way, you will work through production-grade code snippets, full prompts and configs, and "make it better" exercises that encourage you to extend and improve your system. By the final chapter, you will have built an end-to-end AI agent that you can use as a foundation for your own projects. About the reader For Python developers and AI practitioners. All examples run on a standard laptop. About the author Jungjun Hur is an AI and data engineer with experience in e-commerce and AI industries, where he has built production-ready AI applications and LLM-powered features. He is the author of the bestselling book Practical AI Application Development Using LLMs. Younghee Song
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Descrierea produsului
- Use MCP to integrate tools calls into your agent's workflow
- Agentic RAG for relevant responses
- Create memory modules that store facts, context, and evolving goals
- Enable agents to plan, reflect, and self-correct
- Build specialized agents, including a code execution agent
- Design multi-agent systems About the book Build an AI Agent (From Scratch) is a step-by-step guide to creating a working AI agent, starting with the bare essentials and growing your AI into a full-featured, real-world system. You will connect your agent to powerful software tools, implement a reasoning loop, and then extend your agent with retrieval, memory, planning, reflection, and even multi-agent coordination. Along the way, you will work through production-grade code snippets, full prompts and configs, and "make it better" exercises that encourage you to extend and improve your system. By the final chapter, you will have built an end-to-end AI agent that you can use as a foundation for your own projects. About the reader For Python developers and AI practitioners. All examples run on a standard laptop. About the author Jungjun Hur is an AI and data engineer with experience in e-commerce and AI industries, where he has built production-ready AI applications and LLM-powered features. He is the author of the bestselling book Practical AI Application Development Using LLMs. Younghee Song
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