Multi-file changes
Edits across the whole set of files a change actually touches.
Engineering
An AI coding agent that works the task and opens the pull request
Autocomplete helps you type. An AI coding agent does the errand: read the codebase, make the change across the files it touches, run the checks, and open a pull request you can review like any other. Nonilion's coding agent is built for long tasks — it splits work into slices, records every action in an event log, and resumes where it left off instead of losing an hour of context to a timeout.
What is an AI coding agent?
An AI coding agent is a system that completes software tasks autonomously rather than suggesting single lines. It explores a repository, plans a change, edits multiple files, runs tests, and submits the result for human review — operating over a whole task instead of one keystroke.
Built for hours of work, not one request
Real engineering tasks outlast a single model call. The agent divides a task into slices, commits progress as it goes, and writes each step to an append-only log. If a run is interrupted, it resumes from the last good checkpoint with its reasoning intact.
- Task slicing so large changes progress incrementally.
- Checkpointed state that survives interruptions and restarts.
- An event log covering every file read, edit, and command.
- Resumable runs instead of starting the investigation over.
The pull request is the deliverable
The agent's output is a normal pull request in your repository: a branch, scoped commits, and a description of what changed and why. It goes through the same review, the same CI, and the same merge rules as any human contribution. Nothing reaches your main branch without a human approving it.
Review the reasoning, not just the diff
Because every step is logged, a reviewer can see how the agent arrived at a change — which files it read, which approach it abandoned, which command failed. That makes an unexpected diff diagnosable instead of mysterious.
Runs where your team already talks
The coding agent lives in the same workspace as your rooms, so a bug discussed in a standup can become an assigned task without a context switch. Progress is visible while it works, and the finished PR link arrives in the room.
What you get
Long-running autonomy
Slice-based execution with checkpoints for hours-long tasks.
Pull request output
Branch, commits, and description delivered for normal review.
Complete event log
Every read, edit, and command recorded and inspectable.
Resumable runs
A stalled task continues from its last checkpoint.
Your model, your keys
Point the agent at the provider and model you prefer.
Frequently asked questions
Does the AI coding agent push directly to main?
No. It works on a branch and opens a pull request. Your existing review rules, CI checks, and merge permissions all still apply, so a human approves every change that lands.
How is this different from an autocomplete tool in my editor?
Autocomplete predicts the next few lines while you type. A coding agent takes a whole task — explore the repo, change several files, run the tests, open a PR — and works it without you driving each step. They solve different problems and most teams use both.
What happens when the agent gets stuck?
The event log shows the last successful step and what failed after it, so you can either give the agent a correction and resume from that checkpoint or take the branch over yourself.
Can it work on a large existing codebase?
Yes. Task slicing and checkpointing exist specifically so the agent can work through changes that are too large for a single pass, exploring incrementally rather than trying to hold everything at once.
Which models can it run on?
You connect your own provider keys and choose the model, so you can match model capability and cost to the difficulty of the task.
Related capabilities
An agentic AI platform where agents actually finish the work
An agentic AI platform is software that lets AI agents pursue goals autonomously instead of answering one prompt at a time. It gives agents tools, memory, and permission boundaries so they can plan a task, execute multiple steps, recover from errors, and hand back a finished result.
AI agents for teams that share your context, not just your prompts
AI agents for teams are shared autonomous assistants that operate on a team's collective context rather than one person's chat history. They hold defined responsibilities, access shared knowledge and tools, and deliver output into a common workspace so the whole team benefits from the same work.
A BYOK AI platform: your keys, your rates, your model choice
BYOK — bring your own key — means the platform runs model requests through your own provider API keys instead of reselling inference. Your usage is billed directly by the provider at your rates, under your data terms, and you choose which models the platform may use.
Multi-agent collaboration with shared context and one audit trail
Multi-agent collaboration is an architecture where several specialized AI agents work on one objective, each handling part of the problem and sharing context with the others. It improves reliability by narrowing each agent's scope and allowing agents to check or build on each other's output.
Put the coding agent on a task
Connect a repository and assign your first task on the free tier.
