Shipping AI agents that reliably handle complex, end-to-end tasks inside a product remains a challenge. Even agents built on top-tier models struggle to perform consistently enough for deployment, so teams often end up manually building evaluation sets and fine-tuning data.
CopilotKit's new CLiF platform aims to automate that work. It captures agent traces and user actions, then converts them into three outputs: reusable skills, evaluation benchmarks, and fine-tuning data. This turns real usage into a continuous learning loop.
The release addresses a common pattern among teams scaling AI agents: the grind of creating evals by hand. By drawing on actual user interactions, CLiF lets agents improve from experience rather than from manual curation alone.