What are AI Agents? How AI Agents Work | Agentic AI Explained | GenAI Series Ep 17

What is an AI Agent? Learn what AI agents and Agentic AI are, how they differ from traditional AI applications and simple LLM apps, and how the agent loop (plan, act, observe, evaluate) works — explained step by step with a real Resume Analyzer example. Welcome to Episode 17 of the "Generative AI & Agentic AI" Series 🚀 Before we build AI agents, we need a clear mental model of what an agent actually is. In this video, we start from the basics — what is AI? — and build up to AI agents, the agent loop, and Agentic AI. 🔥 You'll learn why an agent is different from a normal AI application: a traditional app follows a predefined flow, while an agent is given a goal and decides for itself which actions to take next. 🎯 In this video, we will explore: 👉 What is Artificial Intelligence (AI)? 👉 What is an AI Agent? 👉 AI Application vs AI Agent 👉 How an AI Agent works, step by step 👉 The Agent Loop 👉 What is Agentic AI? 📚 Topics Covered 📌 What is AI, with real examples (image recognition, speech recognition, recommendations, fraud detection, NLP) 📌 What is an AI agent, and why the "goal" is the key idea 📌 Traditional AI application vs AI agent 📌 Determine goals: how an agent receives and understands a goal 📌 Planning and reasoning: breaking a goal into smaller tasks 📌 Acquire information: databases, APIs, documents, the internet, and other agents 📌 Take action: how agents use tools to perform tasks 📌 Observe and evaluate the result: deciding if the goal is achieved 📌 The agent loop: plan → act → observe → evaluate → repeat 📌 What is Agentic AI 📌 Challenges: data privacy, bias, transparency, job displacement, and ethics 👨‍💻 This video is useful for: ✅ Beginners learning AI agents ✅ Developers moving from LLM apps to Agentic AI ✅ Anyone preparing to build with LangChain and LangGraph ✅ Students learning Generative AI ✅ Engineers exploring multi-agent systems