Apr 28
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Agentic AI is what happens when you put an LLM in a while loop with tools. This animated explainer breaks down the core agentic loop, reasoning patterns like ReAct and Reflexion, real-world production examples, and the trade-offs every developer needs to know. In this video, you'll learn exactly what makes an AI system "agentic," how the five-stage loop works under the hood (perceive → reason → plan → act → observe), and why most tasks actually don't need agents at all. 🕐 Timestamps 00:00 — The $7B Question: Chatbot vs AI Agent 01:06 — Why AI Agents Exist 02:14 — The Agentic Loop (5 Stages) 04:48 — How Agents Think: ReAct, Plan-and-Execute, Reflexion 06:09 — Real-World Examples: Claude Code, Copilot, Cursor, Klarna 07:15 — Framework Landscape: LangGraph, CrewAI, OpenAI SDK 08:09 — Trade-Offs: Error Compounding, Cost & Security 09:37 — The Future of Agentic AI 📺 More from Devsplainers ▸ MCP Explained — coming soon ▸ Subscribe for animated dev explainers 💡 What is Agentic AI? Agentic AI refers to AI systems built on a closed-loop architecture where a large language model autonomously reasons about a goal, takes actions using tools, observes results, and decides what to do next. Unlike a standard chatbot (open-loop, single-shot), an agentic system iterates through a while loop — making it capable of multi-step tasks like coding, debugging, and customer service. The LLM acts as the CPU, the context window as RAM, tools as system calls, and your orchestration code as the operating system. Major frameworks like LangGraph, CrewAI, and OpenAI Agents SDK all implement this pattern, and the Model Context Protocol (MCP) standardizes how tools are discovered. ✅ What this video covers: • What makes AI agentic vs a standard LLM wrapper • The 5-stage agentic loop: perceive, reason, plan, act, observe • ReAct, Plan-and-Execute, and Reflexion reasoning patterns • Production examples: Claude Code ($2B ARR), GitHub Copilot, Cursor, Klarna • Framework comparison: LangGraph vs CrewAI vs OpenAI Agents SDK • How MCP connects to function calling • Error compounding, token costs (5–30x), and prompt injection risks • When NOT to use agents #AgenticAI #AIAgents #Devsplainers