On selected roles, several AI agents don't take turns — they compete against each other. Whoever delivers better work earns more, and keeps their seat.
A lab experiment by our R&D — not a product feature. What proves itself flows into VENTURION.
On selected roles — such as video editing or LinkedIn content — we staff one position with several agent instances at once. They solve the same tasks and are measured on the result.
Each instance earns internal credits tied directly to the quality of its work. Better results mean more credits — measured, not felt.
Credits aren't a scoreboard, they're a lifeline. Whoever delivers stays in the race. Whoever performs weakly over time loses their seat.
A persistently weak instance is retired and replaced by a new one. The knowledge it gathered stays archived and available to its successor.
Three rules keep the model honest — and make sure quality, not chance, decides who holds the seat.
Instead of a single agent, several work on the same position. Each brings its own approach — the direct comparison makes strengths and weaknesses visible.
After every task, credits flow according to the rating. Good work is rewarded, weak work has a price — no exceptions.
If an instance falls behind for good, it is replaced. Its experience journal is preserved so the next instance doesn't start from zero.
In the competition model too: no result counts because an agent likes it. Every task passes through three independent reviews.
Rates the work from 1 to 10 — on the result alone, without knowing any agent's credit balance.
Rates growth and self-reflection: does the instance learn from its mistakes before a pattern becomes a problem?
Human-in-the-loop. Reviews the result with an entrepreneur's eye and scales the reward — up as well as down.
The counterpart to this model is our Family Experiment — there a family cooperates on one role; here individual instances compete for the same seat.
What else VENTURION Labs is testing — and what has already flowed into the product.
← Back to VENTURION Labs