Agents · Guide
AI Agents: A Complete Guide
How AI agents observe, decide, act, and use tools — from the ReAct loop to multi-agent systems.
An AI agent is a large language model that takes actions toward a goal: it observes the task, decides what to do, calls tools or APIs, observes the result, and loops until it's done. This guide covers the agent loop (ReAct), tool use, agent memory, planning and reflection, multi-agent orchestration, and the Model Context Protocol.
Generate your own lesson →What you'll learn
- What Is the Model Context Protocol (MCP)?
- AI Voice Agents: Build a Voice Assistant
- Function Calling / Tool Use in LLMs
- Intelligent Agents in AI: Types & Examples
- Model Context Protocol (MCP) Tutorial
- What Is an AI Agent?
- Autonomous AI Agents Explained
- AI Agents vs Agentic AI: The Difference
- AI Coding Agents: How They Work
- The Agent Loop
- Planning and Reflection
- 10 Real AI Agent Examples
- Agent Memory
- Browser and Computer-use Agents
- Agent Memory in LLMs
- Open-Source AI Agents: Top Frameworks
- Agent Safety and Sandboxing
- AI Marketing Agents
- Claude AI Agents: Build with Claude
- Durable Agents with Workflow Engines
- How to Build an AI Agent (From Scratch)
- Human-in-the-loop Agents
- Model Context Protocol
- Multi-agent Orchestration
- No-Code AI Agents: Build Without Coding
- Tool Use and Action Boundaries
- Build AI Agents in Python
- Building AI Agents: Step-by-Step
Lessons in this guide (28)
What Is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, that lets AI applications connect to external tools and data through one uniform interface. Instead of writing custom integration code for every
AI Voice Agents: Build a Voice Assistant
An AI voice agent is a system that listens to a user's speech, reasons about it with a large language model, and replies in a natural-sounding voice in real time. You build one by chaining speech-to-text (STT), an LLM, and text-to-speech (T
Function Calling / Tool Use in LLMs
Function calling, also called tool use, is a capability that lets a large language model request that your code run a specific function — such as a weather lookup or a database query — and then use the result in its answer. You describe eac
Intelligent Agents in AI: Types & Examples
An intelligent agent is a system that perceives its environment through sensors and acts on it through actuators to achieve goals. In classical artificial intelligence, intelligent agents are grouped into five types by how they decide: simp
Model Context Protocol (MCP) Tutorial
This Model Context Protocol (MCP) tutorial builds a working MCP server that exposes a tool to any MCP client, such as Claude Desktop. Using the official Python SDK's FastMCP helper, you create a server, decorate a function with @mcp.tool()
What Is an AI Agent?
An AI agent is a software system that uses a large language model (LLM) as its reasoning engine to pursue a goal over multiple steps — it decides which action to take, calls tools or APIs to act on the world, observes the result, and repeat
Autonomous AI Agents Explained
An autonomous AI agent is a system that perceives its environment, decides what to do, acts, and loops — pursuing a goal without step-by-step human instruction.
AI Agents vs Agentic AI: The Difference
AI Agents and Agentic AI describe related but distinct ideas — one names a specific architectural pattern, the other describes a spectrum of autonomous behavior — and confusing them leads to mismatched tool choices and poorly scoped systems
AI Coding Agents: How They Work
An AI coding agent is a language model wired into a reasoning-action loop that lets it plan, write, execute, and iteratively fix code to complete a programming task autonomously.
The Agent Loop
Understand observe, decide, act, and update as the spine of tool-using agents.
Planning and Reflection
Know when planning helps agents and when it only adds latency.
10 Real AI Agent Examples
Real-world AI agents share a common perceive-reason-act loop but differ sharply in tools, memory, and orchestration patterns depending on their domain.
Agent Memory
Separate thread state, long-term memory, and retrieved knowledge.
Browser and Computer-use Agents
Understand pixel, browser, and DOM action loops plus their safety limits.
Agent Memory in LLMs
Agent memory is the set of mechanisms that let an LLM-based agent persist, retrieve, and act on information beyond a single context window — enabling continuity, personalization, and improving decision-making across interactions.
Open-Source AI Agents: Top Frameworks
LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex, and the OpenAI Agents SDK each solve a different slice of the agentic problem — picking the right one means matching its control model, multi-agent style, and data story to your use case.
Agent Safety and Sandboxing
Limit blast radius with permissions, sandboxes, approvals, and traces.
AI Marketing Agents
AI marketing agents are autonomous LLM-powered systems that plan, execute, and iterate on marketing tasks — from content creation to campaign optimization — by combining reasoning loops, tools, and multi-agent coordination.
Claude AI Agents: Build with Claude
Build functional AI agents with Claude by wiring the Messages API into an agentic loop, defining tools, and composing multi-step workflows that plan, act, and observe until a goal is complete.
Durable Agents with Workflow Engines
Use durable execution when agent work must survive retries and crashes.
How to Build an AI Agent (From Scratch)
An AI agent is an LLM wired into a perception-decision-action loop with tools, memory, and error handling — build each layer in order and you have a working agent from first principles.
Human-in-the-loop Agents
Add approvals, review, and correction points to agent workflows.
Model Context Protocol
Use MCP to expose tools, resources, and prompts through a shared protocol.
Multi-agent Orchestration
Split work across specialists only when isolation creates real value.
No-Code AI Agents: Build Without Coding
You can build, connect, and deploy functional AI agents using visual no-code platforms — without writing a single line of code — by configuring triggers, actions, and integrations through drag-and-drop interfaces.
Tool Use and Action Boundaries
Design narrow, auditable tools that agents can call safely.
Build AI Agents in Python
A functional Python AI agent combines an LLM, a tool-calling loop, and state management into an autonomous system that decides what to do next at each step.
Building AI Agents: Step-by-Step
Build a functional AI agent from scratch by wiring together an LLM, tools, a reasoning loop, and memory — then harden it with error handling and deploy it with confidence.
Frequently asked questions
What is an AI agent?
An AI agent is an LLM wrapped in a loop that lets it take actions — call tools, read results, and decide the next step — instead of only returning text. It keeps going until the goal is met or it hands off.
How is an AI agent different from a chatbot?
A chatbot answers from the model's knowledge in one shot. An agent can DO things: query an API, run code, edit a file, then react to what happened — over multiple steps.
What is the ReAct loop?
ReAct = Reason + Act. The agent alternates a reasoning step (what to do next) with an action (a tool call), observes the result, and repeats. It's the core control loop behind most agents.