Kore.ai launches Autoloop for continuous enterprise agent optimization

Kore.ai launched Autoloop on October 7, 2026, making the optimization engine generally available to all customers of the Artemis edition of its Agent Platform. Rather than confining testing and tuning to the build stage, the product is designed to keep evaluating agents after deployment.

Kore.ai says Autoloop runs a continuous build, evaluate, diagnose, optimize and re-verify cycle. Before launch, it can build test coverage from a company’s operating procedures. Once deployed, production interactions can trigger another optimization cycle against the same configured goals.

According to Kore.ai, those goals can cover task completion, accuracy and grounding, adherence to business rules, safety, consistency, user experience and cost efficiency. The company says each proposed change is scored against every configured goal before it is retained. A change intended to reduce token use, for example, is also checked for regressions in task completion or safety.

The Artemis platform page describes two components intended to support more targeted changes. StateTrace records an agent’s execution path across handoffs, state, tool calls and context to locate where and why a goal was missed. Agent Blueprint Language, or ABL, maps steps in that trace back to structured agent definitions. That allows Autoloop to modify the component associated with a failure instead of changing the agent broadly.

Kore.ai presents the loop as part of a shared layer for building, deploying, managing, governing and optimizing agents against business-defined goals. The emphasis shifts from manually tuning an agent during development to operating and refining it throughout production.

The launch materials do not provide pricing, deployment prerequisites, adoption figures or measured before-and-after results. Kore.ai’s claims about reliability, safety, cost, latency and performance have not been validated by an independent benchmark or customer study in the supplied evidence.