Kore.ai details how Autoloop scores and rewrites enterprise agents against seven business objectives
The AMW Read
An established agent-platform vendor adds a closed-loop evaluation and optimization layer. This is an incremental update in the agent-ops sub-segment and has no customer outcome data yet.
Kore.ai details how Autoloop scores and rewrites enterprise agents against seven business objectives
Kore.ai has made Autoloop generally available. Autoloop is an optimization engine for the Artemis edition of its Agent Platform. It builds, evaluates, diagnoses and refines agents against seven objectives: task completion, accuracy and grounding, business-rule adherence, safety guardrails, behavioral consistency, end-user experience and cost efficiency. Before deployment, it generates tests from a customer's operating procedures. After deployment, live interactions trigger new optimization rounds. Two components make this work. StateTrace records handoffs, tool calls and state changes, and Kore.ai says it runs on a patent-pending five-layer validation design. Agent Blueprint Language (ABL) turns routing, rules and guardrails into an executable state machine, so a fix can target only the part of the agent that failed.
Our October 8 report covered the launch but said measured customer outcomes were not disclosed. That is still true. The new details show Kore.ai's real argument: production agents fail in ways teams cannot trace. Kore.ai's own survey says 79% of enterprises have reversed an action taken by an agent and 70% have hit failures they could not trace. If those numbers hold, the hard part of running agents is observability and controlled change, not building the agent. Kore.ai is betting that whoever owns the agent's runtime structure, here the ABL state machine, can also own the improvement loop. Standalone evaluation tools cannot easily copy that.
For buyers, the key question is lock-in. Autoloop can only edit agents written in ABL, so automatic tuning is tied to Kore.ai's own agent format. The only evidence offered so far is internal: about 6,500 agent-generated commits a month on a 2.6M-line codebase, with 68 guardrails. Enterprises should ask for audited before-and-after numbers from customers on cost and reversal rates before committing.
