Adaline

Adaline helps AI teams improve their agents autonomously, so you can find fires before they happen, ship fixes in minutes, and feel confidence in their agents — for the first time. It ingests production traces and groups recurring agent patterns into behavior maps your team can inspect, enabling focused reviews and faster iteration from production data.

Adaline generates evals from detected behaviors and user feedback, so regressions are caught before agent changes ship. It provides a structured way to observe how agents perform, what patterns emerge, and how those patterns map to evaluation criteria.

Adaline generates production-faithful synthetic data for the scenarios you keep meaning to cover and the ones you never knew existed. This data supports testing and improvement while aligning with real-world use cases, helping teams broaden coverage without sacrificing realism.

Adaline is the ML layer between your traces and your agent diffs — behaviors, evals, data. Cursor and Claude are the “make no mistake” guesses; this is the feedback loop grounded in real signals. It operates continuously, assisting teams by automating parts of the improvement cycle and keeping changes aligned with actual production signals.

Use cases

• Ship AI agents that learn and self-improve from production logs.