CopilotKit's CLiF Turns Live Agent Traces Into Skills, Evals and Fine-Tuning Data

CopilotKit released CLiF, a continual learning platform that collects traces from AI agents running in production and the actions users take alongside them, then turns that raw activity into three outputs: reusable skills, evaluation sets and training data for fine-tuning. The company positions the tool for teams that already have agents in front of real users but cannot get them to complete complex, multi-step tasks inside a specific product reliably enough to ship.

The problem CLiF targets is familiar to anyone who has tried to move an agent from demo to deployment. Even agents built on top-tier models struggle with end-to-end workflows that depend on a particular product's data, tools and conventions. Teams respond by hand-writing evaluations for their most important flows, a process that routinely runs into the hundreds or thousands of test cases and consumes engineering time that could go toward the product itself.

CLiF's approach is to make that loop continuous rather than a one-time push. Production traces and user interactions become the raw material, and the platform feeds them back as skills the agent can reuse, evals that reflect what users actually do, and fine-tuning data drawn from real sessions. That closes the gap between what a model does in a benchmark and what it does inside a customer's application.

CopilotKit has built its business around in-app AI agents and the developer tooling that connects them to front-end interfaces, so CLiF extends an existing stack rather than arriving as a standalone experiment. The pitch lands in a crowded space: observability vendors, eval startups and agent frameworks all want to own the feedback loop between live usage and model improvement. CLiF's bet is that traces and user actions belong in the same system that produces skills and training data, not scattered across separate dashboards.

The details developers will want are still thin. The announcement did not disclose pricing, whether CLiF runs in the customer's own environment or as a hosted service, which model providers it supports for fine-tuning, or how user actions are captured and governed. Those specifics matter because trace data from production agents often contains customer information, and any platform that turns it into training data inherits the compliance questions that come with it.

What to watch next is whether CLiF can show measurable lifts on the workflows teams care about, not just a cleaner pipeline. If it shortens the cycle from a failed production run to a new eval and a retrained model, it could change how agent teams staff their quality work. If the outputs are noisy, it becomes one more dashboard competing for attention.