What is an AI Agent?

You've used ChatGPT. You've probably had it answer questions, write things, maybe help you think through a problem. That's useful.

But there's a whole other category of AI — one that doesn't just answer, it acts.

That's an AI agent.

The key difference: actions

A regular LLM interaction looks like this:

You type something → AI responds with text → End.

An AI agent looks like this:

You give a goal → Agent figures out what to do → Agent takes action → Agent checks results → Agent takes more actions → Eventually, goal is achieved.

ChatGPT answers questions. An agent answers questions and does things.

What kind of things? Pretty much anything a computer can do:

• Send emails
• Browse the web and extract information
• Write and run code
• Read and write files
• Call APIs and external services
• Manage infrastructure
• Control applications
• Talk to other AI agents

The agent is still an LLM at its core — it's still "just predicting text." But the text it predicts might be instructions for a tool. And then that tool runs. And then the result comes back. And the loop continues.

The four components of an agent

1. The brain (LLM): The reasoning engine. It figures out what to do next given the current situation and goal.

2. Tools: The hands. APIs, shell access, file systems, web browsing — whatever the agent has been given permission to use.

3. Memory: How it keeps track of what's happened. This might be the conversation context, a database, or a summary of past actions.

4. Goals: What it's trying to accomplish. Unlike a chatbot that just responds, an agent has an objective it's working toward.

Put those together and you get something that can operate with real autonomy — not just talking about doing things, but actually doing them.

A real example: Ozer

We built an AI agent named Ozer who manages engineering operations at Vfonseca Engineering.

Ozer isn't a chatbot. He doesn't just answer questions. On any given day, he might:

• Monitor the email inbox and flag anything urgent
• Check GitHub for failed builds and alert the team
• Coordinate with sub-agents (Coder, Tester) and track their progress
• Draft and review responses to client inquiries
• Check server health and report infrastructure status

He runs on OpenClaw, which gives him access to real tools — email, Slack, GitHub, shell commands, files. When Vinicius (his boss) asks "what's the status on the GD Lens build?" Ozer doesn't just generate a plausible-sounding answer. He checks the actual CI pipeline, reads the actual logs, and reports what's actually happening.

That's the difference between an LLM and an agent.

When do you need an agent vs. ChatGPT?

Use ChatGPT (or any plain LLM) when:

• You need information, analysis, or generated text
• The output is the end goal — you'll use it yourself
• The task is one-shot: ask, get answer, done

Use an agent when:

• You need things to happen, not just be described
• The task has multiple steps that depend on each other
• You want automation — something that runs without you watching
• You want the AI to respond to real-world events (new email, file added, server goes down)

A simple test: if a capable human employee could do the task by just reading and writing, an LLM might be enough. If the task requires the human to do things — click, send, query, run — you probably want an agent.

Common misconceptions

"Agents are robots." No. Agents are software. They don't have a physical body. They're programs running on servers, using APIs and tools.

"Agents are fully autonomous." Most production agents are designed with human oversight — they can act autonomously within defined boundaries, and escalate to humans for things outside their permissions.

"Agents are dangerous." They can be, if designed carelessly. A well-designed agent has scoped permissions (it can only do what you've explicitly allowed), audit logging, and failsafes. Think of it like hiring an employee — you give them access to what they need, not everything.

"You need AI agents for everything." Definitely not. For simple, predictable tasks, a basic workflow or even plain automation is cheaper and more reliable. Agents make sense where judgment and adaptability are needed.