AI Agents Explained: How Autonomous AI Works
AI agents go beyond chat — they observe, plan, act, and iterate in a loop until a task is done. From booking flights to writing and running code, agents are the reason AI is moving from "answer questions" to "do things." This path breaks down the agent architecture: how the agent loop works, the design patterns behind reliable agents, what multi-agent systems look like, and critically, how to keep humans in control when autonomous AI makes mistakes.
What You'll Learn
- 1What defines an AI agent and how it differs from a chatbot or a workflow
- 2The agent loop: observe, think, act, and how agents decide when they are done
- 3Design patterns like ReAct, plan-and-execute, and reflection that make agents reliable
- 4Multi-agent systems: when one agent is not enough and how agents collaborate
- 5Function calling and tool use — the mechanism that lets agents interact with the real world
- 6Failure modes, guardrails, and human-in-the-loop strategies for safe autonomous AI
Curated Lessons (9)
Free, interactive lessons you can complete on your phone in 5-10 minutes each.
What Is an Agent?
Agents — AI That Takes Action
The Agent Loop
Agents — AI That Takes Action
Agent Design Patterns
Agents — AI That Takes Action
Harness, Skills & How Agents Learn
Agents — AI That Takes Action
Multi-Agent Systems
Agents — AI That Takes Action
When Agents Fail — And How to Keep Humans in Control
Agents — AI That Takes Action
The Limitation — and the Breakthrough
Function Calling & Tool Use
What Is Function Calling?
Function Calling & Tool Use
The Tool Use Loop
Function Calling & Tool Use
Ready to start learning?
Join thousands learning AI on AI Sprout. Free, interactive, mobile-first.
Start Learning FreeRelated Topics
Model Context Protocol (MCP): The Universal AI Connector
Understand MCP — the Model Context Protocol — and how it standardizes AI tool integration. Learn servers, clients, resources vs tools, and how to build with MCP.
What Is RAG? Retrieval-Augmented Generation Explained
Understand RAG (Retrieval-Augmented Generation) from the ground up. Learn embeddings, vector search, chunking strategies, and how to give AI your own data.