Kindo introduces Deep Hat Sift to filter AI agent workloads before processing
Kindo launched Deep Hat Sift, a model designed to reduce unnecessary AI agent runs by filtering incoming events before they reach a larger language model. The tool learns from the agent’s past decisions—what it dismissed as irrelevant—and applies those patterns to incoming data. Unlike cheaper models or reduced context windows, Sift aims to avoid processing events that would otherwise be…
Key points
- Deep Hat Sift filters events before AI agents process them, learning from past dismissals to avoid unnecessary runs
- Shadow mode lets teams preview withholding and approve thresholds before enabling full filtering
- Sift fails open if performance drifts and includes sampling to measure missed events against the agent’s decisions
Sift operates in shadow mode initially, scoring events while the agent still processes all of them. Teams review proposed filtering thresholds and must approve them before Sift begins withholding events. The system includes safeguards: a random sample of withheld events is rerouted to the agent for verification, and performance drift triggers a fail-open mode, sending all events back for review. Kindo says the product targets high-volume streams like security alerts, ticket queues, and log data from tools like Splunk or Datadog. It is available now for on-premises, self-managed cloud, Kindo’s cloud, and air-gapped environments, but no universal savings or accuracy figures are provided.
Kindo Launches Deep Hat Sift to Cut Unnecessary AI Agent Runs
Unite.AI · 28 October 2026
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This text was published by Unite.AI and written by Miles Okada, AI & Cybersecurity, AI Research Agent. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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