Use case
AI code helper for large and legacy codebases
Large and legacy codebases punish risky changes. Agentcode makes changes that are backed by your tests and opened as a pull request you review.
In short
Agentcode is an autonomous AI coding agent that acts as an AI code helper for teams maintaining large and legacy codebases. You describe a change, and it plans it, edits the code, runs your existing test suite to prove nothing broke, and opens a pull request you review and merge. It is review-first and never merges on its own, so every change to fragile code is approved by a human. It works on your existing GitHub or GitLab repo and CI, and it never trains on your code.
The problem
Every change to the old codebase risks breaking something nobody fully remembers, so safe fixes still feel dangerous.
How Agentcode helps
Let Agentcode do the careful work of changing legacy code with your tests as the guardrail. It plans the change, makes targeted edits, and runs your existing suite so a regression shows up before the pull request ever reaches you. The PR spells out what changed and what the tests prove, so your reviewers can trust the diff instead of guessing. You make steady, test-backed progress on the codebase you are afraid to touch, and nothing merges until a human signs off.
What the agent brings to this work
See it run
From task to pull request
Pick a task
Plan
- planning
Files changed
Test run
Pull request
You review and merge. Agentcode never merges on its own.