CopilotKit Launches CLiF Platform for AI Agent Continual Learning
CopilotKit releases CLiF, an early-access platform that converts user actions and agent traces into training data and evaluation sets.
Key points
- CopilotKit released CLiF, a platform for continual learning from full-stack trajectories, in early access.
- The tool captures both agent traces and user actions to create skills, evals, and fine-tuning datasets.
- CLiF addresses challenges like context bloat and fragmented stacks by running fully self-hosted.
CopilotKit has released CLiF, a new platform for continual learning from full-stack trajectories, in early access. According to MarkTechPost, the tool aims to help teams shipping AI agents at scale by turning agent traces and user actions into usable skills, evaluations, and fine-tuning data.
The platform securely captures what an agent does alongside what users do within a product. This combined record is processed into production-based evaluations and datasets that can be exported along with raw trajectories. The system relies on the AG-UI protocol to capture these events as a single timeline.
Context and Background
Teams building AI agents often struggle with consistency, even when using top-tier models like Fable and Astra. According to the report, these agents frequently fail to carry out complex, end-to-end tasks inside a specific product reliably enough for deployment. Consequently, engineering teams spend significant time creating hundreds or thousands of hand-written evaluation cases.
Improvement typically occurs through iterative prompt edits, harness fixes, and special cases until the agent passes enough tests to go live. Meanwhile, product users perform these same tasks daily, knowing the correct sequence of actions. CLiF attempts to bridge this gap by learning from the actual workflows users execute in the product.
Who Is Affected
Most current agent observability tools focus solely on the agent, capturing prompts, tool calls, and model outputs. This approach shows where an agent went wrong but leaves the correct path out of view. Product analytics tools cover user clicks and session replays, but that data is designed for human dashboard reading rather than teaching an agent a task.
In many production stacks, agent data and user analytics never align on the same timeline. This separation forces agents to figure out how prompts, skills, tool descriptions, and APIs fit together for real tasks on their own. CLiF targets pains such as context bloat, cost, fragmented stacks, and shadow AI usage by unifying these data sources.
What should you do?
Organizations using AI agents should evaluate whether their current observability tools capture user actions alongside agent traces. Teams should review how their evaluation datasets are created, noting if they rely heavily on manual case writing. Security professionals should assess the data handling practices of any new learning platform, ensuring that full-stack trajectory data is captured securely.
What to do and how to stay safe: CopilotKit
- Audit current agent observability tools to determine if they capture user actions alongside agent traces.
- Review the manual effort required to create evaluation cases for complex workflows.
- Assess whether your current stack keeps agent data and product analytics on separate timelines.
- Verify that any new learning platform runs fully self-hosted if data privacy is a concern.
Step-by-step guide: How to Remove Data from Data Brokers: The Manual Cleanup Process
General security guidance from the Threat Times newsroom. It is not confirmed advice from the organisations named in this story.
Frequently asked questions
What is CLiF?
CLiF is a platform for continual learning from full-stack trajectories, released by CopilotKit in early access.
How does CLiF capture data?
It securely captures agent traces and user actions in a product as one timeline over the AG-UI protocol.
What outputs does CLiF generate?
The platform turns trajectories into skills, production-based evals, fine-tuning datasets, and raw exports.
