What Is an AI Agent? (And How to Build One)
An AI agent is a system where a language model decides its own steps and uses tools to reach a goal. What that means in practice, how agents differ from workflows, and three routes to building one.
By The AcademyGems desk
The short answer
An AI agent is a system where a language model decides its own next steps and uses tools, like search, email or a database, to reach a goal. Anthropic defines agents as systems where LLMs "dynamically direct their own processes and tool usage". You can build one with no code in n8n or in Python.
A plain definition
A chatbot answers you. An agent acts for you. Give an agent a goal, and it decides which steps to take, calls tools to take them, looks at the results and decides what to do next, until the job is done or it gets stuck.
Anthropic's engineering team draws the line clearly in its guide to building effective agents (December 2024): workflows are "systems where LLMs and tools are orchestrated through predefined code paths", while agents are "systems where LLMs dynamically direct their own processes and tool usage".
Agents vs workflows
That distinction matters more than the hype suggests:
- A workflow follows a fixed path you designed: step one, step two, step three. Predictable, cheaper, easier to test.
- An agent chooses its own path. More flexible, but less predictable and harder to control.
Anthropic's advice is to use agents "for open-ended problems where it's difficult or impossible to predict the required number of steps, and where you can't hardcode a fixed path". For everything else, a workflow is usually the better build. Many things sold as "AI agents" are really workflows, and that is often fine.
What an agent is made of
Most agents combine the same parts:
1. A model that reasons about the goal and picks the next step 2. Tools it can call: web search, email, a spreadsheet, a CRM, code execution 3. A loop that feeds each result back to the model so it can decide again 4. Memory or knowledge, often a RAG pipeline that lets it look things up in your own documents
Examples you can actually build
The courses reviewed on this website build agents like these:
- A sales agent that drafts and sends outreach emails
- A research team of agents that investigates a topic in depth
- A voice agent that answers a real phone number using your own knowledge base
- A multi-agent sales system that finds leads, logs them in a CRM and books meetings
How to build one: three routes
1. No code, in n8n. If you do not program, build agents visually. AI Builder is Ed Donner's three-week n8n course covering agents, voice agents and MCP, and the free AI Automation Society community is built around n8n.
2. In Python, properly. Start with the foundations in the AI Engineer Core Track (LLM engineering and RAG), then build agents across the OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP in the AI Engineer Agentic Track.
3. Into production. Once an agent works on your laptop, the AI Engineer Production Track covers deploying it with monitoring, guardrails and security.
If you want a free first step before paying, AI Agent Builders on Skool gets you to one working agent.
The reviews behind this guide
AI Builder: Create Agents, Voice Agents & Automations in n8n
Ed Donner's 14-hour no-code course on Udemy: AI agents, ElevenLabs voice agents, Supabase RAG and MCP, all built in n8n across three weekly projects, ending in a multi-agent sales system. 4.8 from 5,285 ratings and 42,179 students, with Bestseller and Highest Rated badges, updated September 2026. Built for entrepreneurs and agency owners as much as engineers.
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents
Ed Donner's hands-on course building generative-AI products across 20+ models, covering RAG, QLoRA fine-tuning and agents through many real projects. Aimed at developers who want to ship, not just watch.
AI Engineer Agentic Track: The Complete Agent & MCP Course
Ed Donner's 21-hour agentic AI course on Udemy: a program its subtitle pitches as 30 days, building eight real projects across the OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP, ending in a trading floor run by four agents and six MCP servers. 4.7 from 50,227 ratings and 412,044 students, updated September 2026. The deepest agent curriculum AcademyGems has found on Udemy.
Frequently asked questions
- What is the difference between an AI agent and a chatbot?
- A chatbot answers a question and stops. An agent works toward a goal: it decides its own steps, uses tools such as search or email, and keeps going until the task is done.
- What is the difference between an AI agent and a workflow?
- A workflow follows a fixed path someone designed. An agent decides its own path. Anthropic recommends agents for open-ended problems where the steps cannot be predicted in advance.
- Can I build an AI agent without coding?
- Yes. Tools like n8n let you build agents visually. The AI Builder course on Udemy and the free AI Automation Society on Skool both teach the n8n route.
- What should I learn first to build AI agents in Python?
- The basics of working with language models and RAG first, then agent frameworks. Ed Donner's Core Track and then his Agentic Track follow that order.
Source Anthropic: Building effective agents. AcademyGems guides may link to affiliate partners; see the methodology.