The most interesting shift in AI right now isn't bigger models — it's what happens when several AI agents work a problem together: one researching, one building, one checking the others' work. Done right, the team dramatically outperforms any single agent. Done naively, you get very confident chaos.
A mob is not a team
Spin up five agents on one task with no structure and they duplicate work, contradict each other, and amplify each other's mistakes with total confidence. The value isn't in the number of agents; it's in the choreography — who does what, who checks whom, and when a human gets the final word.
Our research keeps returning to three principles. Verification beats generation: an agent whose only job is finding flaws in another's output catches a remarkable share of errors. Narrow roles beat general ones: 'review this for security' outperforms 'review this' every time. And the human stays at the decision points that matter — agents propose, people dispose.
Why a studio does research
This isn't academic curiosity. The pipelines we prototype in the lab quietly power how we build client work — which is why a small studio ships at a pace that surprises people. Every experiment that survives becomes process; every process compounds.
The frontier is trust calibration: knowing which decisions can be safely delegated to an agent team and which must never be. Getting that boundary right, we suspect, will matter more than any individual model release.