{"version":1,"type":"story","url":"https://digestai.news/story/kindo-introduces-deep-hat-sift-to-filter-ai-agent-workloads-before-pro","json":"https://digestai.news/story/kindo-introduces-deep-hat-sift-to-filter-ai-agent-workloads-before-pro.json","markdown":"https://digestai.news/story/kindo-introduces-deep-hat-sift-to-filter-ai-agent-workloads-before-pro.md","slug":"kindo-introduces-deep-hat-sift-to-filter-ai-agent-workloads-before-pro","headline":"Kindo introduces Deep Hat Sift to filter AI agent workloads before processing","summary":"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 ignored, cutting costs without sacrificing coverage.\n\nSift 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.","keyPoints":["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"],"whyItMatters":"For enterprises running AI agents at scale, unnecessary workloads add hidden costs. Sift shifts the focus from cheaper models to smarter routing, potentially cutting inference spend without sacrificing coverage—though trade-offs in recall and risk remain.","category":{"slug":"enterprise","name":"Enterprise & Industry","url":"https://digestai.news/category/enterprise"},"entities":{"companies":["Kindo"],"models":["Deep Hat Sift"],"people":["Troy Presley"]},"firstPublishedAt":"2026-10-28T19:58:52Z","updatedAt":"2026-10-28T19:58:52Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"Unite.AI","title":"Kindo Launches Deep Hat Sift to Cut Unnecessary AI Agent Runs","url":"https://unite.ai/kindo-deep-hat-sift-ai-agent-event-triage","publishedAt":"2026-10-28T19:58:52Z","type":"press","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Kindo introduces Deep Hat Sift to filter AI agent workloads before processing\", 28 October 2026, https://digestai.news/story/kindo-introduces-deep-hat-sift-to-filter-ai-agent-workloads-before-pro","publisher":"Digest AI","title":"Kindo introduces Deep Hat Sift to filter AI agent workloads before processing","datePublished":"2026-10-28T19:58:52Z","url":"https://digestai.news/story/kindo-introduces-deep-hat-sift-to-filter-ai-agent-workloads-before-pro"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}