Kore.ai Launches Autoloop to Continuously Optimize Enterprise AI Agents

Kore.ai has launched Autoloop, an optimization engine that evaluates and updates enterprise AI agents after deployment using production interactions as new evaluation signals.

The system measures agents against business-defined goals, traces failures and applies targeted changes. It validates proposed updates against all configured goals before retaining them, using deterministic checks alongside model-based evaluation.

Autoloop’s StateTrace system records handoffs, state changes, tool calls and context across an agent’s execution path. Agent Blueprint Language (ABL) then maps those trace steps to the instructions and constructs that produced them, allowing individual components to be changed without rewriting the full agent.

The system can optimize latency, cost and conversation quality. Its continuous lifecycle approach is designed to cover agent development, testing and post-deployment improvement within the same platform.

Autoloop is part of the Artemis edition of the Kore.ai Agent Platform, launched May 21, 2026, initially on Microsoft Azure. Artemis is built for developing, governing and optimizing multiagent systems. Its architecture includes ABL and Arch, an AI agent architect that converts business objectives into agent systems.

The launch comes as enterprises face challenges in monitoring autonomous software. Kore.ai’s 2026 Agent Productivity Index surveyed 408 IT and engineering leaders actively running AI agents in production. The survey found that 79% had reversed an AI-agent action, 70% had experienced a failure they could not trace, and 72% said their agents introduced unmanaged financial or compliance risk.

“Enterprise AI is entering its third wave, where governance, observability, and trust define success at scale,” Raj Koneru, CEO and founder of Kore.ai, said.

“To scale AI with confidence, enterprises need a standardized agent building system and the enforcement of robust governance,” Koneru said.