A team of AI agents that runs a process on its own
An autonomous company, orchestrated by agents
Set a goal, and a 'company' of AI agents — each with a job — divides the work, hands it off between them, and produces the result with little steering from a person.
A research demo of agents coordinating → a system you can actually run.
| Before | After | |
|---|---|---|
| Coordinating many agents | a research topic, impressive in demos | something you can actually run end to end |
| Keeping agents on task | they drift, loop, or lose the goal | coordination logic holds the group to one objective |
| Human involvement | constant supervision, step by step | set the goal, let it run, check in |
| What it tells you | a paper to read about what might be possible | a live measure of what agents can do right now |
This is the experiment crossing from a demo you watch to a system you can run. The real question it probes: how far can a team of agents coordinate before a person has to step in? That line keeps moving as the underlying AI gets better — so the project doubles as a hands-on gauge of where this technology is actually heading. It is a working probe, not a finished product.
What I built
A system that runs an autonomous "company" of AI agents toward a single goal. Instead of one all-purpose assistant, it sets up a group of specialized agents — each handling one kind of work — and coordinates them like an org chart pointed at an objective.
- Roles. Each agent has a defined job rather than trying to do everything. Splitting the work this way keeps any one agent from getting overloaded or confused.
- Hand-offs. Work moves from agent to agent — the output of one becomes the input of the next — so a process can actually flow through the group.
- Coordination logic. This is the part that keeps the group from falling apart: the rules that hold every agent to the same goal so the team doesn't go in circles or lose the thread. ("Multi-agent orchestration" is just the technical name for getting several agents to work together toward one outcome.)
- Minimal steering. You set the objective and let it run, stepping in only where a person is genuinely needed rather than supervising every move.
It is a fork-and-extend experiment — I took an existing open project and pushed it toward the edge of what autonomous coordination can currently do. It is honest R&D, not a packaged product.
Why it matters
If a team of agents can reliably run a real process on its own, the leverage is enormous — you describe an outcome and a coordinated group of specialists carries it out. That is the promise this experiment is built to test, hands-on rather than in theory.
It is also a measuring stick. The amount of human steering this needs keeps dropping as the underlying models get more capable, so running it is a way to see exactly where the frontier of agent coordination sits right now — and where it is heading next. Knowing that firsthand, instead of guessing from a demo reel, is the whole point.
- 01Set the goal
You give the system a single objective — the outcome you want the 'company' of agents to reach.
- 02Assign roles
The work is split across specialized agents, each handling one kind of task rather than one agent trying to do everything.
- 03Coordinate
Agents pass work between each other, and the coordination logic keeps everyone aimed at the same goal instead of wandering off.
- 04Produce
The agents carry the process forward and produce the result, with a person stepping in only where it is genuinely needed.
- 05Measure the frontier
Each run shows how far autonomous coordination can be pushed today — a moving line that gets watched as the models improve.
This is research you can run rather than just read — a working system that shows how far a team of agents can coordinate on its own right now. As the models improve, the amount of human steering it needs keeps falling, which makes it a useful early read on where agentic systems are heading.