Kindo releases Deep Hat Sift to cut calls to a large AI model

Kindo sells a control center for AI agents. It released Deep Hat Sift, which is available at this time. Sift is a small model that sits in front of alert queues, logs and tickets. It selects the items that need a large language model. Kindo tells that this decreases the cost of agents. It gave no test results and no savings figure.

How it works

Each Sift model serves one data source, one agent and one filter task. A large company can operate hundreds of thousands. It works in the customer environment, also with no external network. A person must give approval for the threshold first. If quality drifts, it sends all items to the model.

The numbers

Kindo gives no numbers for its own tool. It quotes Gartner, which predicts that by 2028 a global Fortune 500 company will have more than 150,000 agents, up from less than 15 in 2025.

What is not known

The source gives no data on how many items Sift skips by error. It gives no price. The claim that it gets smarter with each decision has no data in the text.

Insight: The next fight on agents is the cost of each task

In October 2026, AWS and UC Berkeley released Infinity, which stops an agent while it waits for a tool. Its authors tell that about 75,000 agents fit in 8 GB. Sift prevents model calls. Both attack the cost of agents, one in memory and one in model use, which seems to show that cost, not model score, is the next fight. Tell your teams to give the cost of each finished task.