Kore.ai's Autoloop grades every change to a live AI agent against all of a team's goals at once

Anyone on an operations team will recognise Kore.ai's account of how agents get maintained. A failure comes in and someone fixes it by hand, one at a time ^[3]. Patching one problem often opens up another, according to the company ^[3]. Its example is token spending. Cut it, and safety or the agent's ability to finish the job can slip without anyone noticing ^[7].

Teams tend to tell themselves that each fix stays local and that the agent they shipped is still the agent they have. Kore.ai's index points the other way: only about one in five enterprises in it did not report reversing an action an agent took ^[17]. The index counts reversals ^[4]. The claim that hand patching causes them is Kore.ai's own argument, built on its own survey ^[3].

The pitch is an agent that tunes itself. The useful part is a scoring gate. Every proposed change is checked against the whole goal set at once, so a cheaper version fails if it costs safety or task completion ^[7]. StateTrace finds where a goal slipped by reading the production trace step by step ^[9]. The Agent Blueprint Language compiles routing, business rules and guardrails into an executable state machine ^[11]. Each step in the trace maps to one part of the blueprint, so Autoloop rewrites only that part ^[11]. A five-layer validation architecture makes most of the checks deterministic ^[10]. The company says this keeps round-the-clock optimization affordable at enterprise scale ^[10].

"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 ^[12].

The product is for companies already building agents on Kore.ai's platform ^[1]. Kore.ai counts more than 500 Global 2000 organizations as customers ^[16]. It also uses the approach on itself. AI agents make about 6,500 commits a month to its 2.6 million-line production codebase, and 68 guardrails stay switched on the whole time ^[13]. Raj Koneru, the founder and chief executive, said the businesses that succeed in scaling AI "will be the ones using AI to build, govern and optimize AI" ^[14]. The launch describes agents being adjusted automatically after deployment ^[2]. It does not say whether a person signs off on a change before it reaches customers.

Before switching Autoloop on, I'd sort agents on two tests, both taken from Arikala's line. The first is whether the team can trace a failure to one step. The second is whether it can write each goal as a threshold a machine can score. An agent that passes both is a candidate, because the gate then has a number to defend on every goal. Where only the trace test passes, a person still writes the fix, now with better evidence. Where only the goals test passes, evaluation comes first, because an optimizer cannot rewrite a step it cannot find ^[9]. An agent that fails both needs tracing and written goals before any optimizer touches it. The tradeoff is in the goal set. The gate protects only what the team wrote down, so a cheaper change can still break any business rule left out of the goals ^[7].