Kayba

Kayba is a platform designed to help users improve the reliability and performance of AI agents by identifying failures and verifying that fixes are effective. It addresses the challenge of shipping changes to AI agents without proof that the fixes actually resolve the underlying issues, moving beyond traditional dashboards and pre-defined evaluation suites. The tool focuses on providing concrete evidence that each fix works, based on real failure patterns observed in production environments.

Kayba operates by integrating with existing trace and error storage systems such as Sentry, PostHog, and OpenTelemetry. Once connected, it listens for errors and automatically converts each failure into a custom evaluation—a reproducible test grounded in the agent’s actual failure traces, code, and context. This enables users to see not only that a failure occurred, but also why it happened, with detailed traces and root cause explanations available in plain English. Users can interact with Kayba through Slack or a terminal to ask questions about failures and receive cited traces as evidence.

When a failure is detected, Kayba can propose fixes and generate pull requests that include the relevant trace and error information. Users have the option to merge, modify, or write their own fixes, while Kayba continues to monitor the agent’s performance by running the custom evals against new traces. The platform tracks the pass rate of each fix over time, allowing users to observe improvements or regressions immediately after changes are deployed. This workflow aims to reduce the time from identifying a failure to verifying a fix to under eight minutes.

Kayba is suitable for teams or individuals responsible for maintaining and improving AI agents, particularly those seeking to automate the detection, diagnosis, and verification of agent failures using real-world data.