Kore.ai, the global leader in enterprise AI platform and agentic applications, today announced the general availability of Autoloop™. The optimization engine is part of the Kore.ai Agent Platform, Artemis edition. This launch brings AI agent optimization to enterprises at production scale. Businesses set the goals, and Autoloop builds the agents. It then measures them against those goals and keeps optimizing them automatically, from first draft through production. As a result, enterprise AI shifts from hand-tuned agents to agents that self-improve.
Why Manual Fixes Fall Short
Most enterprises still maintain agents by fixing one failure at a time. However, a fix for one problem often creates another. The 2026 Kore.ai Agent Productivity Index shows the cost of this approach. It found that 79% of enterprises have reversed an action taken by an AI agent. Moreover, 70% have faced a failure their teams could not trace. Meanwhile, the AI agent optimization industry is moving in a new direction. Gartner predicts that autonomous learning techniques will appear in a majority of AI agents by 2030, up from less than 5% in 2026.
“Every enterprise knows what it wants from its agents: finish the job, follow the rules, stay safe, and do it at a sensible cost,” said Raj Koneru, Founder and CEO of Kore.ai. “With Autoloop, your agents keep improving against the goals you set. The companies that scale AI will be the ones using AI to build, govern, and optimize AI.”
Autoloop Starts From Goals, Not Prompts
Teams first define what success looks like across the dimensions that matter to the business. Autoloop then optimizes against all of them at once. These goals include:
- Task completion: The agent finishes what the user came to do and hands off to a human only when it should.
- Accuracy and grounding: Every answer is correct and backed by the enterprise’s own data, with no fabricated facts.
- Business-rule adherence: The agent complies with policies, eligibility checks, and limits on every interaction.
- Guardrails and safety: The agent avoids data exposure, off-policy actions, and unsafe responses.
- Consistency: The agent behaves the same across phrasings, languages, channels, and edge cases.
- End-user experience: Users get fewer turns, less repetition, and lower latency across chat and voice.
- Cost efficiency: The agent delivers the same outcome with fewer, faster, and more affordable model calls and tokens.
Against AI agent optimization these goals, Autoloop runs one continuous loop. The loop covers build, evaluate, diagnose, optimize, and re-verify. Before launch, it builds the agent and its test coverage from the enterprise’s own operating procedures. Then it iterates until the agent meets every goal. In production, real interactions start new optimization cycles. Additionally, Autoloop scores every change against all goals at once. Therefore, a gain on one goal, such as lower token spend, cannot quietly hinder another, such as task completion or safety.
StateTrace and ABL Power Automatic Optimization
Automatic optimization requires complete visibility into agent activity. It also requires control over how agents change. Two Kore.ai innovations provide these capabilities.
StateTrace AI agent optimization delivers visibility. It evaluates agents against their full production execution context. It traces every handoff, delegation, state, tool call, and piece of context across the agent network. Consequently, Autoloop knows where and why a goal was missed. A patent-pending five-layer validation architecture makes most checks deterministic. This keeps continuous optimization affordable at enterprise scale.
Agent Blueprint Language (ABL) delivers the control. It compiles supervision, routing, handoffs, delegation, tools, business rules, and guardrails into an executable state machine. Every step in a trace maps back to the blueprint. Thus, Autoloop changes exactly the part that caused the miss, and nothing else.
Kore.ai applies the same discipline to its own engineering. AI agents there now produce roughly 6,500 commits a month on a production codebase of 2.6 million lines. In addition, 68 always-on guardrails govern that work.
“You can’t optimize what you can’t see, or fix precisely what you can’t express precisely,” said Prasanna Arikala, Chief Technology Officer and Chief Product Officer at Kore.ai. “StateTrace lets Autoloop see exactly what agents did, and ABL allows it to change anything that needs changing. These are the technologies that make automatic optimization a reality.”
Today’s launch extends the ground Kore.ai has staked out in the market. Much of the industry sells tools that create agents, or control layers added after the fact. In contrast, Kore.ai builds the harness. This single layer builds, deploys, manages, and optimizes enterprise AI agents, with governance defined from the first line. Autoloop completes it, because only an agent built and governed in one layer can be optimized in that same layer, automatically and with evidence.
Autoloop is available now to all customers of the Kore.ai Agent Platform, Artemis edition.
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News Source: Businesswire.com
