An agent ships a change. TrueClara runs it as a randomized canary against control, measures the real production outcome with always-valid sequential statistics, and returns a calibrated verdict — keep, roll back, iterate, or abstain — over a reward API and MCP server. Every verdict compounds into an experiment corpus.
When an agent ships a change, TrueClara starts the experiment itself: a randomized split between the new code and control, scoped to the routes the change can actually touch.
Always-valid sequential statistics measure the real production outcome and return a calibrated verdict — keep, roll back, iterate, or abstain — the moment the data can support one, never before.
Each measured experiment makes the next one faster to call. The corpus is the moat: agents get a sharper reward signal the more they ship through it.
When an agent ships a change, TrueClara starts the experiment itself: a randomized split between the new code and control, scoped to the routes the change can actually touch.
Always-valid sequential statistics measure the real production outcome and return a calibrated verdict — keep, roll back, iterate, or abstain — the moment the data can support one, never before.
Each measured experiment makes the next one faster to call. The corpus is the moat: agents get a sharper reward signal the more they ship through it.
The canary, the verdict, and the corpus live in one loop — so the reward signal an agent gets is real, not a proxy. It also flags broken routes and dead ends along the way — table stakes, not the headline.
Randomized canary, always-valid statistics, calibrated verdict, compounding corpus — one reward loop for every agent-shipped change.