Agents are repeating software’s dumbest mistake! The real moat in 2027 won’t be anything to do with the agent. Instead, it’ll be by capturing fullstack trajectories. Imagine two teams ship the same agent, on the same model, on the same day. Your competitor ships it and moves on. But you ship agents that learn from every session: where users stepped in, what they did by hand, what they keep asking for. Every week it picks up a few new skills, evals and datasets. Six months later, your aren't really competing on the same product anymore. Because the advantage isn't in the model but rather it's in your users' data. CLiF is a new learning platform from @CopilotKit that sees every trajectory: → The trace: every step the agent took → The product: everything the users did The AG-UI protocol already sits between the agent and the UI, so it sees both sides. And then turns real sessions into exportable fullstack trajectories, evals, skills and fine-tuned models. The visual below explains how it works and I worked with the CopilotKit team to put this together. You can self-host it, so the moat stays yours. Access it here: go.copilotkit.ai/get-clif My suggestion if you’re building user-facing agents: start collecting this data now. CLiF already implements the entire continual learning infrastructure you need to achieve that. More details below.