{"slug": "ai-agents-in-practice-read-from-the-beginning", "title": "AI Agents in Practice — Read from the beginning", "summary": "This article is a practical, production-oriented guide to building AI agents, focusing on engineering patterns rather than hype or specific products. It explains why agent demos often fail in production, defines an agent as a control loop with tools, state, and boundaries, and introduces three core primitives: MCP for acting, RAG for knowing, and Skills for reusable procedures. The series is actively maintained and also includes companion guides on the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG).", "body_md": "A practical, production-oriented guide to building AI agents — patterns over products, anti-hype, vendor-neutral.\nPart 1: The Demo Worked. Production Didn't.\nPriya's refund went through on a shipped order. The model was right. The system around it wasn't. Why agent demos break the moment they meet production — and what the demo hid that production reveals.\nPart 2: What Makes Something an Agent\nDefine what an agent actually is in engineering terms — a control loop with tools, state, and boundaries. The three primitives an agent composes (MCP for acting, RAG for knowing, Skills for following reusable procedures). The bridge from manual ReAct to native tool calling.\nPart 3: How the Loop Actually Works\nComing soon. What happens turn by turn when the agent runs. State that carries across turns, stopping conditions as real decisions, and context as a finite engineering resource — not just a bigger window.\nThis series is actively maintained. New parts will be linked here as they publish.\nMCP in Practice — Read from the beginning\nThe Model Context Protocol from first principles — what MCP is, why it exists, and how to build production-grade tool servers and clients.\nRAG in Practice — Read from the beginning\nRetrieval-augmented generation from first principles — why AI gets things wrong, what RAG fixes, and how the full pipeline works.", "url": "https://wpnews.pro/news/ai-agents-in-practice-read-from-the-beginning", "canonical_source": "https://dev.to/gursharansingh/ai-agents-in-practice-read-from-the-beginning-1l5l", "published_at": "2026-05-23 06:08:33+00:00", "updated_at": "2026-05-23 06:31:19.327920+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "developer-tools", "enterprise-software"], "entities": ["Priya", "MCP", "RAG", "ReAct"], "alternates": {"html": "https://wpnews.pro/news/ai-agents-in-practice-read-from-the-beginning", "markdown": "https://wpnews.pro/news/ai-agents-in-practice-read-from-the-beginning.md", "text": "https://wpnews.pro/news/ai-agents-in-practice-read-from-the-beginning.txt", "jsonld": "https://wpnews.pro/news/ai-agents-in-practice-read-from-the-beginning.jsonld"}}