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CopilotKit's release of CLiF addresses a critical bottleneck in scaling AI agents by automating the conversion of agent traces and user interactions into fine-tuning datasets and evaluation metrics. This tool directly enhances the lifecycle of production-grade AI systems, enabling continuous improvement through data derived from real-world agent performance rather than static benchmarks.

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CopilotKit Releases CLiF: A Continual Learning Platform That Turns Agent Traces and User Actions into Skills, Evals, and Fine-Tuning Data

MarkTechPostOctober 15, 20261 min read

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 […] The post CopilotKit Releases CLiF: A Continual Learning Platform That Turns Agent Traces and User Actions into Skills, Evals, and Fine-Tuning Data appeared first on MarkTechPost.

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