Reseña del libro "AI Agents - Foundations (en Inglés)"
AI AGENTS: FOUNDATIONSBuild production-ready AI agents with Python, LangChain, and LangGraph.What does it actually take to build an AI agent that can do more than generate text?An agent needs to reason, use tools, retrieve information, maintain context, handle failures, protect itself against malicious input, and operate reliably outside a laboratory environment. Understanding these pieces individually is not enough. The real challenge is learning how they work together as a production system.AI Agents: Foundations takes a practical, engineering-first approach to agent development. Instead of presenting disconnected examples or abstract theory, the book follows NexaCore, a fictional software company whose support agent evolves throughout the book from a small Python prototype into a deployable, monitored, tested, and security-hardened production system.You will learn how to: - Understand the architecture of modern AI agents and the difference between a language model and an agent system- Build your first working agent in Python using the ReAct reasoning loop- Design and integrate real tools that connect agents to APIs, databases, files, and external services- Implement short-term and long-term memory- Build Retrieval-Augmented Generation (RAG) systems that ground agent responses in current documentation- Engineer prompts for reliable agent behaviour- Design planning and multi-step execution strategies- Handle failures, retries, timeouts, and unexpected tool results- Add logging, tracing, and observability to understand what your agent is actually doing- Test agent behaviour systematically instead of relying on demonstrations- Defend production agents against prompt injection and other security threats- Apply least-capability principles and layered security controls- Prepare an agent for real deploymentThe book covers the complete journey from the first working agent to production readiness. Every major concept is connected to the NexaCore project, so architectural decisions are introduced in response to concrete engineering problems rather than presented as isolated patterns.You will work with Python, LangChain, LangGraph, model APIs, external tools, vector retrieval, databases, testing infrastructure, logging, and security controls.No previous experience with LangChain, LangGraph, or LLM APIs is required. You should be comfortable with Python, async/await, basic HTTP APIs, and JSON.By the end of the book, you will understand not only how AI agents work, but how to structure, test, secure, observe, and deploy one as a real software system.AI Agents: Foundations is Book 1 of the AI Agents in Practice series.The journey begins here: build an agent that works.
