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If you’ve been experimenting with agent frameworks and wondering what’s actually happening under the hood, this new book might be up your alley.

Build an AI Agent (From Scratch) by Younghee Song and Jungjun Hur walks through the full process of implementing an LLM-powered agent—from first principles. Instead of relying on opaque libraries or “magic” abstractions, it helps you understand and build every component yourself.

The authors cover core mechanisms like:

• The ReAct loop (Thought → Action → Observation)

• Using MCP to make tool calls part of the agent’s workflow

• Building memory modules for context and long-term goals

• Implementing Agentic RAG for more relevant retrieval

• Adding planning, reflection, and self-correction capabilities

• Extending to multi-agent systems and specialized agents (like code executors)

By the end, you’ll have a fully functional AI agent that can reason, plan, and act — not just call APIs. The examples use open, inspectable code you can adapt for your own frameworks or research setups.

If you’re into building or tuning AI agents for production, research, or experimentation, this one feels like a practical deep dive into the “why” and “how” of agentic systems.

• Full details: Build an AI Agent (From Scratch)

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