Introduction
Teams shipping AI agents at scale keep encountering the same bottleneck. Even agents built on top-tier models struggle to perform complex, end-to-end tasks within a specific product consistently enough for deployment. As a result, teams grind out evaluations for their most critical workflows—often hundreds or thousands of hand-written cases—then iteratively improve the agent through prompt edits, harness fixes, and special-case handling until it passes enough tests to go live.
Meanwhile, the product's own users perform those same tasks every day, and they know exactly which actions to take and in what order. CopilotKit, the company behind the open-source CopilotKit framework and the AG-UI protocol, has released CLiF, a platform for continual learning from full-stack trajectories, now available in early access. CLiF securely captures what the agent does and what users do in the product, then transforms that record into skills, production-based evals, and fine-tuning datasets—all exportable alongside...
Key Takeaways
- The deployment bottleneck persists: Top-tier models still struggle with reliable end-to-end task execution inside specific products, forcing teams into costly manual evaluation and iteration cycles.
- Users already know the workflows: Product users perform the same tasks daily and understand the optimal action sequences—knowledge that traditional agent development pipelines largely ignore.
- CLiF turns activity into assets: CopilotKit's new platform captures both agent and user behavior, converting full-stack trajectories into reusable skills, production-grounded evals, and fine-tuning datasets.
- Early access is open: Teams can begin exploring continual learning from real deployment traces now, with exportable outputs designed to feed directly into agent improvement pipelines.
What This Means for Agent Development in 2026
As we move through 2026, the shift from static, offline evaluation to continual learning from live production trajectories represents one of the most significant trends in agent engineering. Teams are increasingly recognizing that the richest training signal comes not from synthetic benchmarks but from real users completing real tasks. CLiF positions itself at the center of this shift by closing the loop between what agents do, what users expect, and how models should improve. For organizations already invested in the CopilotKit and AG-UI ecosystem, this release offers a natural extension for turning deployment activity into compounding agent capability.
via MarkTechPost