I'm one developer. Aimless takes a recurring operational job described in plain words and runs the work behind it: scheduled tasks, dashboards built from the apps a business already uses, and approvals where it stops and asks a person. The part I care about is what happens after an agent acts. Every action goes into a ledger with the hypothesis behind it. Later a collector reads the number that followed, a deterministic engine compares results across hypotheses, and the measured lift becomes a lesson the next run gets automatically. One variable per cycle, so a result can be attributed. A result that hasn't arrived is recorded as pending, never as zero. The demo needs no account. Pick an industry and you get a workspace dressed as your company in a minute. It calls no model: every run is a recorded trace, labelled Simulation, because an open demo hitting a frontier model per visitor is a bill I can't pay yet. It shows the shape of the product, not proof the agents are good. Private beta. I'd like to hear how others close the loop between what an agent did and whether it worked.

Aimless

AI ops agents that learn from the results of their own work

AI ops agents that learn from the results of their own work