Kalibr, a new company, is tackling a critical issue in AI agent deployment: manual debugging. This process, where humans monitor and fix issues, is a bottleneck that slows down AI systems. Kalibr argues that relying on humans to notice, diagnose, and fix problems is inefficient and unsustainable, especially as AI agents scale to thousands of decisions per hour.

The current setup often leads to degraded performance over time. Agents may start failing more frequently, but these issues aren't immediately apparent. By the time someone notices, it could take hours or days to resolve. This manual process is akin to managing a system from 2008, where engineers watch dashboards and flip switches when things break.

Kalibr proposes a different approach: treating each model and tool combination as a path, with the system automatically routing around issues. This removes the human from the reliability loop, ensuring that the system learns and improves continuously without interruptions. The company believes that as AI agents make more decisions, this abstraction will be crucial for success.

The key is shifting decision boundaries, similar to how packet routing moved into networks and scheduling into control planes. This move allows humans to focus on higher-level tasks, like defining goals and improving strategies, rather than incident response. Kalibr's solution aims to compound intelligence rather than operational debt, giving them a competitive edge in the AI market.