CopilotKit releases CLiF continual learning platform
MarkTechPost: CopilotKit Releases CLiF: A Continual Learning Platform That Turns Agent Traces and User Actions into Skills, Evals, and Fine-Tuning Data
Introduction Teams shipping AI agents at scale keep running into the same pattern. Even agents built on top-tier models like Fable and Astra struggle to carry out complex, end-to-end tasks inside a specific product consistently enough to deploy. So teams grind out evals for the workflows that matter most, often hundreds or thousands of hand-written cases, and then improve the agent bit by bit through prompt edits, harness fixes, and special cases until it passes enough of them to go live.
Get smart on it
CLiF is a continual learning platform that captures what AI agents do and what users do in a product on the same timeline, then converts that data into reusable skills, evaluations, and fine-tuning datasets. Most agent development today relies on hand-written test cases and iterative prompt edits to improve agent performance, but CLiF instead learns from the actual work patterns users already execute in the product every day. The platform operates through a four-stage loop: capturing full interactions, identifying recurring patterns and friction points, having a human review and approve proposed skills, and delivering those skills to the agent without requiring a redeploy. This approach addresses challenges that slow down agent teams, including the difficulty of building comprehensive evaluations, managing costs, and keeping separate data systems in sync.
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