You file an issue before bed: "Migrate the date helpers off moment.js." You wake up to a draft pull request — branch created, files changed, tests green, waiting for review. That is the pitch for an autonomous AI coding agent, and the surprising part is how little of it is novel. The hard problem is not the model. It is the loop around the model: the harness that turns a task into a reviewable PR with nobody in the chair.
We built this pattern and ran it against real repositories. What follows is the architecture that held up, the GitHub wiring that kept it safe, and an honest account of which tasks it finishes and which it quietly botches.
Anatomy of the overnight loop
An autonomous coding agent is a state machine with a language model wired into a few of its transitions. Strip away the marketing and five stages remain:
Ingest — pull the task (a GitHub issue, a queue row, a line in a file) and the repo into a clean working directory.
