Module 1, Lecture 1.1 | Introduction to Agentic Systems
An agent is software that perceives its environment, reasons about what to do, and takes autonomous action to achieve goals. This lecture introduces the perception-reasoning-action loop that underpins every agentic system, distinguishes agents from chatbots, assistants, and copilots, and establishes a critical insight: LLMs generate text — your code executes actions. We trace through the complete agent loop and explore why the 2022–2024 era of large language models made reliable agents possible for the first time.
The Evolution of AI Agents | IBM — A comprehensive look at how AI agents developed over the decades, from early rule-based systems through reinforcement learning to today's LLM-powered agents.
AI Agent - Wikipedia — Broad reference covering the definition, categories, and history of intelligent agents in AI research.
Expert System - Wikipedia — Explains how pre-LLM expert systems like MYCIN and DENDRAL worked, including their knowledge base and inference engine architecture.
Expert Systems in AI - GeeksforGeeks — An accessible, tutorial-style introduction to expert systems covering their components, types, and how forward and backward chaining drive rule-based reasoning.