Kore.ai launches Autoloop to keep tuning enterprise AI agents after they go live

Enterprise artificial intelligence platform company Kore.ai Inc. today launched Autoloop, an optimization engine for the AI agents that customers build on its Kore.ai Agent Platform.

Businesses set the targets, and Autoloop keeps adjusting the agents to hit them automatically, including after they’re deployed.

Autoloop goes after the way most enterprises maintain agents today, fixing failures by hand one at a time. Patching one problem often opens up another, according to Kore.ai. In the company’s “2026 Kore.ai Agent Productivity Index,” 79% of enterprises reported reversing an action taken by an AI agent. A failure their teams could not trace had hit 70% of them.

Customers set up Autoloop by giving it goals covering measures such as task completion, adherence to business rules and cost, and the engine optimizes for all of them at once. Accuracy is one of the goals as well, judged on whether answers are backed by the enterprise’s own data. Every proposed change is scored against the full set, so lower token spending cannot quietly come at the cost of safety or the agent’s ability to finish the job.

Autoloop writes the first version of each agent, tests included, from the operating procedures a company already has, then keeps iterating until every goal is met. Real interactions start fresh rounds of optimization once the agent is in production.