---
type: concept
title: "What Is an AI Agent"
description: "An AI agent is a large language model given tools to interact with the outside world and take actions, making it far more capable than the LLM alone."
tags: [agents, llm, tools, definition]
videos: [learn-90-of-building-ai-agents-in-30-minutes, im-hosting-a-huge-ai-hackathon-competition, ai-agents-the-future-of-ai-or-a-passing-fad, new-llama-3-model-beats-gpt-and-claude-with-function-calling, function-calling-with-any-llm-for-local-ai-agents-feat-langchain-huggingface-and-llama-3, the-only-ai-developer-roadmap-you-need-in-2024, developers-this-is-how-you-stay-relevant-with-ai-ai-agents-masterclass-1, i-will-win-the-nvidia-ai-agents-developer-contest]
created: 2026-07-21
updated: 2026-07-21
---

# What Is an AI Agent

An AI agent is a large language model that has been given the ability to act on the world. Cole's definition is consistent across dozens of videos: "an AI agent is any large language model that is given the ability to interact with the outside world on your behalf through tools" [0:01:44]. The distinction from a plain chatbot is not sophistication, it is capability: "add your first tool because you need a tool otherwise it's really just a regular large language model, not an agent" [0:03:05]. Put another way, "an agent is basically just a large language model that has been augmented with tools to interact with the outside world" [0:03:06], so that "your chatbot can book on a calendar or it can go up to a database to get information to make its response better" [0:03:21].

A slightly fuller definition adds statefulness and interaction between agents: "AI agents are large language models like Claude GPT or Llama that have been given the ability to interact with external service or each other and maintain persistent state to accomplish infinitely more than just an isolated chat bot" [0:00:47]. The concrete payoff is real actions, not text: an agent is what lets you "draft emails or create tasks or add contacts to your CRM whatever it might be" [0:00:19]. That is why Cole treats agents as the point where AI becomes genuinely useful: "almost any real world application that uses AI is only useful if it leverages agents" [0:11:03], and "all of these things that are actually useful and truly powerful are built with agents" [0:00:36].

Because the definition hinges on tool use, the classic teaching build is a task-management assistant that must "invoke many different tools to interact with a SAA to do something rather complex" [0:05:06]. The requirement is strict enough that Cole picks projects specifically to force it: "because we need this AI agent to interact with an external service to really be an AI agent I chose to use Asana" [0:02:27]. Agents are also framed as the most consequential and still-unsettled frontier in AI engineering: "we are definitely in the wild west when it comes to AI agents anything is possible and we are just scratching the surface right now" [0:00:35].

## Builds on

- [Tool Calling](./tool-use.md) - the single capability that turns an LLM into an agent; without a tool it is "just a regular large language model."

## Contrasts with

- [Agents vs. Workflows](./agents-vs-workflows.md) - an agent decides its own actions, distinguishing it from a fixed, hardcoded workflow.

## Related

- [Agent Core Components](./agent-core-components.md) - the model, instructions, and tools that make up an agent.
- [Tool Design](./tool-design.md) - how the agent's tools are shaped and described.
- [Agent Teams](./agent-teams.md) - multiple agents interacting with each other and maintaining shared state.
- [Model Selection](./model-selection.md) - which LLM you build the agent on, including whether it supports tool calling.
- [Memory Systems](./memory-systems.md) - the persistent state that lets an agent do more than a stateless chatbot.
- [Retrieval-Augmented Generation (RAG)](./rag.md) - giving the agent a knowledge base to pull from as a tool.
- [Human in the Loop](./human-in-the-loop.md) - keeping a person in control as agents take real actions.
- [Local & Self-Hosted AI](./local-ai.md) - running agents on models you host yourself.
- [Learn by Building](./learn-by-building.md) - Cole's preferred way to understand agents is to build one.
- [AI Agent Learning Roadmap](./ai-agent-learning-roadmap.md) - Cole's ordered path for learning to build AI agents, from Python and LLM fundamentals through no-code prototyping, coded agents, RAG, and multi-agent architectures.

## Implemented by

- [Claude](../entities/tools/claude.md), [ChatGPT](../entities/tools/chatgpt.md), and [Llama](../entities/tools/llama.md) - the base LLMs Cole names as the brains behind agents.
- [LangChain](../entities/tools/langchain.md), [Pydantic AI](../entities/tools/pydantic-ai.md), and [OpenAI Swarm](../entities/tools/openai-swarm.md) - frameworks used to give those models tools.
- [Asana](../entities/tools/asana.md) - the external service in Cole's canonical "make it a real agent" task-manager builds.

## Sources

- [Learn 90% of Building AI Agents in 30 Minutes](../sources/learn-90-of-building-ai-agents-in-30-minutes.md) - "an AI agent is any large language model that is given the ability to interact with the outside world on your behalf through tools" [0:01:44]
- [AI Agents: The Future of AI or a Passing Fad?](../sources/ai-agents-the-future-of-ai-or-a-passing-fad.md) - "AI agents are large language models like Claude GPT or Llama that have been given the ability to interact with external service or each other and maintain persistent state to accomplish infinitely more than just an isolated chat bot" [0:00:47]
- [Developers, THIS is How you Stay Relevant with AI - AI Agents Masterclass #1](../sources/developers-this-is-how-you-stay-relevant-with-ai-ai-agents-masterclass-1.md) - "all an agent is is some large language model that is given the ability to interact with the outside world able to draft emails or create tasks or add contacts to your CRM whatever it might be" [0:00:19]
- [I Will Win the Nvidia AI Agents Developer Contest](../sources/i-will-win-the-nvidia-ai-agents-developer-contest.md) - "an agent is basically just a large language model that has been augmented with tools to interact with the outside world" [0:03:06]
- [Function Calling with ANY LLM for Local AI Agents (Feat. LangChain, HuggingFace, and Llama 3)](../sources/function-calling-with-any-llm-for-local-ai-agents-feat-langchain-huggingface-and-llama-3.md) - "because we need this AI agent to interact with an external service to really be an AI agent I chose to use Asana" [0:02:27]
- [New Llama 3 Model BEATS GPT and Claude with Function Calling!?](../sources/new-llama-3-model-beats-gpt-and-claude-with-function-calling.md) - "I'm going to give it a very difficult task where it needs to invoke many different tools to interact with a SAA to do something rather complex for me" [0:05:06]
- [The ONLY AI Developer Roadmap You Need in 2024](../sources/the-only-ai-developer-roadmap-you-need-in-2024.md) - "this is probably the step that takes the most time because this is where you really get to unlock the full power of AI with AI agents" [0:12:11]
- [I'm Hosting a HUGE AI Hackathon Competition](../sources/im-hosting-a-huge-ai-hackathon-competition.md) - "we are definitely in the wild west when it comes to AI agents anything is possible and we are just scratching the surface right now" [0:00:35]
