Company / September 16, 2026
Agents improve while they work
Most agents fail the same way every day and nobody fixes it. While. is the loop that catches the failure in traffic, simulates it, trains on it, and measures the gain on a held-out set with a confidence interval.
Every company we talk to that has shipped an agent has run into the same three problems, usually within the first month.
The same mistakes, every day. The agent fails one particular kind of request. Maybe it is a refund with two line items, or a customer whose phone number is not on file. It failed that way on Monday and it will fail that way on Tuesday, and you find out from a support ticket rather than a dashboard.
The fix is a guess. Someone edits the prompt. Maybe it helps. Maybe it quietly breaks something that used to work. There is no way to know until it ships.
There is no proof before it ships. Nobody can show that the agent got better on the thing that was broken, or that it did not get worse on the things that were fine.
Prompts do not learn. Models do. The catch is that learning from your own traffic takes a loop, and most teams have a dashboard instead.
The loop
While. runs next to the agent you already have, and it does four things.
- Watch. Live traffic becomes a map of what the agent handles well and where it struggles.
- Simulate. The weak spots turn into thousands of rollouts, with tools that break on purpose and customers who do not cooperate.
- Train. Every rollout gets a reward, and the ones that pass train the next version of the model.
- Evaluate. You get a paired before and after per task category, on conversations that were kept out of training.
Then it starts again, because the next day's traffic is the next training set.
What changes
A failure gets caught in the traffic the same day instead of arriving as a support ticket a week later.
The model trains on thousands of rollouts of the failure instead of getting a prompt edit and a hope.
You see a before-and-after score before anything ships, not after.
And you end up owning a model that learns your business, instead of renting one that never will.
Where we are
The loop is live for design partners now, with hosted training and your own endpoint at the end of it. The results so far are on the home page, and the method behind the first public one is in We trained a support agent to hand customers off at the right time.
If you have an agent in production and a failure you are tired of seeing, ask for access.