An AI coding agent is a system that takes a goal — "add rate limiting to this API," "fix this bug" — and works toward it in a loop: it plans the steps, writes code, runs it, observes what happened, and decides what to do next. If a test fails, it reads the error and tries again. That loop is the whole idea, and it is what separates an agent from the autocomplete you are used to.
Agent vs. copilot, in one line
A copilot makes you faster at the current line. An agent owns a whole task and reports back with a result. One accelerates the keystroke; the other does the job.
The agent loop
Every capable coding agent runs some version of the same cycle:
- Goal — a task in plain language, a ticket, or a bug report.
- Plan — break the goal into concrete steps (touch these files, add this route, write these tests).
- Act — write code, run commands, read files, call tools.
- Observe — did the tests pass? did the server start? does the output look right?
- Iterate — if something broke, diagnose and try a different approach.
The loop is what makes it "agentic." A model that only emits text once is an assistant. A system that acts, checks its own work, and corrects course is an agent.
Why a copilot can't do this
In-editor assistants like Copilot and Cursor are excellent at their job: suggestions, refactors, inline chat. But they operate inside your editor, on the file in front of you, and a human drives every step. The assistant never independently runs the test suite, reads the failure, and fixes the code — you do that. On any real change, the keystroke is a small fraction of the work; the rest is planning, wiring, testing, reviewing, and merging. A copilot speeds up the small fraction. (We break this comparison down in automatedev vs Copilot and Cursor.)
From a single agent to a platform
A raw agent in a terminal is powerful but hard to trust with real work: there is no gate, no audit trail, no isolation between tasks, and no guarantee it tested anything. That is the gap a coding agent platform fills. It wraps the agent in the things production teams need:
Explicit phases
Plan, build, test, review, secure, deliver — each observable, instead of one opaque "it did stuff."
Human gates
Approval checkpoints at the plan and before merge, so autonomy never means "unsupervised."
Isolation
Each run works in its own workspace, so parallel tasks never collide.
Bring your own model
The agent engine is swappable; the workflow around it stays the same.
What an agent is good at — and what it isn't
Today's agents handle the mechanical span from an approved plan to a tested, reviewed change well — often at the level of a fast junior engineer. What they do not replace is judgment: understanding your domain, deciding what to build, and owning the call to ship. The right model keeps a human as the decision-maker and lets the agent carry the repetitive middle. That is exactly how an autonomous run is structured.
See an AI coding agent run the whole loop.
Book a 30-minute demo and watch an idea go from brief to a tested, reviewed pull request — with a human gate at every checkpoint.
Book a 30-minute demo