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.
Spotting where a goal slipped falls to Kore.ai’s StateTrace evaluation layer. Agents are judged against the full record of what they did in production, down to each handoff, tool call and state change across a network of agents. A five-layer validation architecture makes most of the checks deterministic. The company says that keeps round-the-clock optimization affordable at enterprise scale.
Kore.ai’s Agent Blueprint Language handles the fix. Introduced earlier this year, the language compiles routing, business rules and guardrails into an executable state machine, so each step in a trace maps back to a specific part of the blueprint. Autoloop can then rewrite only the piece responsible for a miss.
“You can’t optimize what you can’t see, or fix precisely what you can’t express precisely,” said Prasanna Arikala, Kore.ai’s chief technology officer and chief product officer. He credited the two technologies with making automatic optimization practical.
The approach behind Autoloop also shapes Kore.ai’s own software development, where AI agents write code. About 6,500 commits a month to the company’s 2.6 million-line production codebase now come from those agents, which work under 68 always-on guardrails. Founder and Chief Executive Raj Koneru said the companies that manage to scale AI “will be the ones using AI to build, govern and optimize AI.”
Autoloop is available now to all customers on the Kore.ai Agent Platform’s Artemis edition, which launched in May. Kore.ai counts more than 500 Global 2000 organizations as customers.