How AI Agents
Actually Work.
If you have ever wondered what makes an "AI agent" different from ChatGPT, this is the page. Plain English. Receipts where they exist. No hype.
If you have ever wondered what makes an "AI agent" different from ChatGPT, this is the page. Plain English. Receipts where they exist. No hype.
An agent is a program that takes a goal, decides which tools to use, performs work in the real world, watches what happens, and adjusts. A chatbot only produces text. An agent does things.
You type a question. It produces an answer. The interaction ends when you close the tab. Nothing has changed in your files, your calendar, or your codebase.
You describe a goal. It opens your files, runs commands, calls APIs, writes new code, deploys it, then reports back. The work persists after you close the tab.
Every modern agent runs the same 4-step loop. Once you see it, you cannot unsee it.
The model reads the current goal, the chat history, and the list of tools available. It decides what to do next. If the goal is "ship a landing page," the next step might be "read the existing index.html to learn the style."
The model calls a tool. Tools are functions like Read, Write, Bash, Search, Fetch. Each tool returns structured data (file contents, command output, search results).
The tool's output gets fed back to the model. If a command failed, the model sees the stderr. If a file did not exist, the model sees the error. The model now knows more than it did one step ago.
The model picks the next action. Loop back to step 1. The agent keeps looping until the goal is met, you cancel it, or it asks for permission to do something risky.
This varies by setup, but a productive coding agent usually has these. We teach each one in workshops linked at the bottom.
File ops
Read, Write, Edit
View and modify any file in the project
Shell
Bash
Run commands like npm test, vercel --prod, ls
Search
Grep, Glob
Find code by content or filename pattern
Web
Fetch, Search
Pull docs, check API status, research
External tools
MCP servers
Connect Gmail, Notion, Stripe, your DB
Sub-agents
Task
Spawn a focused sub-agent for one job
Traditional software development requires a human to know which file to open, which API to call, which command to run, in which order. The skill ladder is long: syntax, then frameworks, then ops, then deployment.
An agent compresses that ladder. You describe the outcome. The agent already knows the syntax, the frameworks, the deploy commands. What you bring is judgment: what to build, what good looks like, when to stop.
That is the operator role. It is not coding. It is also not "no code." It is something new, and it pays.
If you already code, an agent does not put you out of a job. It changes what your job is. Instead of writing every line, you direct the agent to write the lines, then review them at the diff level. Speed goes up. Errors go down. The work feels less like typing and more like reviewing a junior engineer who is fast, careful, and never tired.
Three things, in this order:
Ready to direct one?
30 minutes. You bring the task. We ship your first agent live, on your screen, in your environment. Credit applied to any tier if you continue.