Kindo launches Deep Hat Sift to decide which events trigger an AI agent run

Kindo announced Deep Hat Sift on October 28, a model that decides which incoming events warrant an AI agent run before the larger model starts processing them, according to a report from Unite.AI.

How Sift works

An AI agent that reads every security alert or log entry incurs a cost even when its conclusion is that nothing needs doing. At enterprise volumes, those uneventful investigations can become a substantial workload of their own, per the report.

Sift is positioned ahead of the agent in the pipeline. It learns from the verdicts of the agent it serves and then filters events, so that the larger model is not invoked for every alert or log entry.

Details

Pricing, context window, parameter count, benchmark scores and availability terms were not part of the information available at the time of writing. Kindo's own announcement should be consulted for technical specifications.

What this means

Sift targets the cost of agent runs that end with no action needed. Placing a filter model in front of the agent addresses that cost at the point where events enter the pipeline. Because Sift learns from the verdicts of the agent it serves, its filtering is tied to that agent's own conclusions.