Multi-Step AI Agents
Systems that extend LLMs with actions, tools, and memory to complete complex tasks autonomously.
From Chatbot to Autonomous Worker
A basic LLM can only generate text. An AI agent can think, plan, use tools (search the web, write code, query databases), observe results, and loop until the task is done. This is the architecture behind tools like Claude Code, AutoGPT, and Devin.
Agents represent the leap from "AI as a text generator" to "AI as a capable assistant that takes action."
Thought → Plan → Action → Observation → repeat until goal is achieved or budget is reached.
Web search, code execution, file I/O, APIs, databases — agents can interact with the real world.
Loop stops when: goal achieved, max iterations hit, or no more progress detected.
Watch the Agent Loop
The agent cycles through these steps. Click any step to jump to it:
Loop repeats until goal is achieved or budget is exhausted
Agent Architecture
Real-World AI Agents
Reads files, writes code, runs tests, commits to git — all autonomously in an agentic loop.
AI software engineer that plans, codes, debugs, and deploys entire features using the agent pattern.
GPT-4 with tools (browse web, run code, use DALL-E) is an agent using the same loop architecture.
Test Your Understanding
Q1.What makes an AI agent different from a basic chatbot?
Q2.What are the four steps in the agent loop?
Q3.When does an agent loop typically stop?