Kore.ai has introduced Autoloop, an optimization engine designed to automatically adjust enterprise AI agents after their deployment. The tool addresses common maintenance challenges, as 79% of enterprises report having to reverse AI agent actions, while 70% have encountered failures that teams could not trace, according to the 2026 Kore.ai Agent Productivity Index.
Users define specific goals for Autoloop, such as task completion rates, adherence to business rules, cost efficiency, and accuracy based on internal data. The engine evaluates proposed changes against these metrics to ensure that cost reductions do not compromise safety or performance. It utilizes the StateTrace evaluation layer to monitor agent activity in production.
The system uses the Agent Blueprint Language, introduced earlier this year, to map agent actions to specific business rules. This allows Autoloop to rewrite only the necessary components when a goal is missed. The company employs this technology internally, with AI agents contributing approximately 6,500 commits per month to its 2.6 million-line codebase under 68 guardrails.
Autoloop is currently available to all customers using the Artemis edition of the Kore.ai Agent Platform, which was launched in May. Kore.ai reports that its client base includes more than 500 organizations from the Global 2000 list.
