AI coding agent: what it is and how it works

An AI coding agent is software that takes a described task, plans an approach, edits your codebase, runs your tests, and opens a pull request for you to review and merge. Unlike an autocomplete tool that suggests the next line, an agent works through a whole unit of work and produces a reviewable diff. The defining trait is autonomy with a checkpoint: the agent does the work, but a human reviews and merges, so nothing reaches your main branch without approval.

That distinction matters because it changes where the human spends attention. With autocomplete you accept or reject keystrokes; with an agent you review outcomes. The unit of trust moves from the token to the pull request.

From autocomplete to chat to autonomous agents

It helps to see the progression in three stages, because each one asked the developer to do something different:

• Autocomplete assistants predict the next few lines as you type. They are fast and stay inside your editor, but they only act when you are already writing the code yourself.
• Chat assistants answer questions and draft snippets you paste back in. They widened the scope from a line to a block, but you are still the one wiring the result into the project.
• Autonomous coding agents take a task instead of a prompt. They read the relevant files, make a plan, write the change across multiple files, run the test suite, and open a PR. You move from author to reviewer.

The shift is not just convenience. An agent that can run your tests and read failures can iterate on its own work before you ever see it, which is something a suggestion box cannot do.

The task to plan to edit to test to PR loop

Every Agentcode run follows the same loop, and understanding it is the fastest way to understand what an agent actually does:

1. Task. You describe what you want in plain language: fix this bug, add this endpoint, refactor this module. You can attach an issue or a failing test.
2. Plan. The agent inspects the repository, identifies the files involved, and writes a short plan. The plan is visible before any code changes, so you can redirect early.
3. Edit. The agent makes the changes across the codebase, following the conventions it sees in your existing files.
4. Test. The agent runs your existing test suite and CI checks. If something fails, it reads the output and revises the change rather than handing you broken code.
5. PR. The agent opens a pull request with the diff, the plan, and the test results attached. You review and merge.

You can see this loop step by step on how it works, and the individual capabilities are broken out under code generation, automated testing, and bug fixing.

Why review-first matters

The most important design choice in a coding agent is what it is allowed to do without you. Agentcode is review-first: it never merges on its own. Every change arrives as a pull request on your existing GitHub or GitLab repository, runs through your CI, and waits for a human to approve it.

The agent does the work. You keep the merge button. That single boundary is what makes autonomy safe to adopt on a real codebase.

Review-first also means the agent fits the workflow your team already trusts. You are not learning a new dashboard or a new place where code lives. The diff shows up where every other diff shows up, with the same approvals, branch protections, and required checks. If you want to compare this discipline against tools that behave differently, see Agentcode vs Devin and Agentcode vs GitHub Copilot.

Where github copilot and other assistants stop

Inline assistants are excellent at what they do, but they share a limitation: they help you write code, they do not own a task end to end. They will not read a failing CI run and fix it, open a PR, or carry a multi-file change to completion on their own. A common github copilot limitation is exactly this gap between suggesting code and shipping a reviewed change. An agent fills that gap, which is why teams often run both: an assistant for live editing and an agent for the backlog work nobody wants to babysit.

What an agent does not do

A credible agent is honest about its boundaries. Agentcode does not merge without review, does not train on your code, and does not replace engineering judgment. It is good at well-scoped, verifiable tasks: bug fixes, test coverage, dependency bumps, mechanical refactors, and small features. It is not a substitute for design discussions or architectural decisions. The review step is where your judgment enters, and it is deliberately not optional.

Who benefits

Solo developers use an agent to clear backlog while they focus on the hard part of the product; see solo developers. Engineering teams use it to keep the boring-but-necessary work moving without pulling senior people off feature work; see engineering teams. In both cases the value is the same: more shipped pull requests, with the same review bar you already hold. If you want to try the loop on your own repo, look at pricing or read our guide to reviewing an AI pull request.