Kindo, the run-anywhere AI agent management platform that puts AI to work and keeps the enterprise in control, today announced Deep Hat Sift, a self-improving AI model that decides which events in high-volume alerts, tickets, and logs need a large language model and skips the rest. Until now, enterprises faced a costly choice: pay for an LLM to review every event, or keep AI agents off their busiest sources altogether. Deep Hat Sift removes that trade-off. Agents run only where there is work to do, and every skipped event is one an LLM never has to process.
Deep Hat Sift is a System One decision model, the fast, inexpensive class of AI that answers yes or no rather than generating text. Cheap filters have always existed; the problem is trusting what they skip. Fixed-weight models are only as good as the day they shipped and must be retrained and re-released to improve. Sift learns from the verdicts of the agent it serves, so with every decision it gets better at judging what that agent would have found, and skipping an event becomes a decision an operations team can stand behind. A person sets each threshold once, and every filtered event is recorded for transparency and compliance.
“Enterprises are scrambling to rein in runaway LLM costs because they’ve been locked into an unfair binary choice. Either run a large model on everything and sacrifice speed and cost control, or run a simple, fixed-weight model and accept that you cannot trust what it ignores,” said Ron Williams, CEO of Kindo. “Deep Hat Sift gives IT, Security, Finance, and Compliance teams a new world of choice to put intelligent agents to work where cost once made it impossible.”
Agents that triage events are already among the first to reach production, but the economics break on high-volume sources. Reviewing every event at the volumes a SIEM, log platform, or busy ticketing system produces gets expensive fast. To control costs, many enterprises have kept agents away from their busiest sources, which is where the high-value work often is. That trade-off blocks essential innovation and process optimization.
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That is the gap Sift closes. It runs first in shadow mode, scoring every event while still sending every event to the agent, and learning from the agent’s verdicts which events it dismisses. Those verdicts are the only training data Sift needs, so there is no labeling project for the team to run and no retrained vendor model to wait for. It then previews, on the customer’s own events, what it would filter, and begins filtering only after a person approves a threshold with a written reason. A random sample of events that would have been withheld continues to go through, so the miss rate is always measured.
Deep Hat Sift is a purpose-built model, assigned to a data source, a target agent, and a filtering task to deliver the best possible performance outcome. A large enterprise can run hundreds of thousands of Sift models at once, each becoming the expert on its own stream and providing significant savings.
Source: PRNewswire