CopilotKit Releases CLiF: A Continual Learning Platform That Turns Agent Traces and User Actions into Skills, Evals, and Fine-Tuning Data
Quick Answer
CopilotKit has launched CLiF, a continual learning platform designed to transform agent traces and user actions into actionable skills, evaluations, and fine-tuning data.
Quick Take
This addresses the challenges faced by teams deploying AI agents, particularly those built on models like Fable and Astra, which often struggle with complex tasks. By automating the evaluation process, CLiF aims to streamline the development of AI workflows.
Key Points
- CLiF automates the conversion of agent traces into skills and evaluations.
- Targets challenges in deploying AI agents at scale with complex tasks.
- Designed for teams using top-tier models like Fable and Astra.
- Aims to reduce the need for extensive hand-written evaluations.
- Facilitates continual learning for improved AI performance.
Article Excerpt
From source RSS / original summaryIntroduction 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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