A developer named Rasmic has published a workflow for recursively improving an OpenClaw agent, meaning the agent gets systematically smarter through repeated, structured iteration rather than one-off tweaks.
The core claim is that this process is both simple and repeatable, which matters because most agent improvement strategies are ad hoc and do not compound. A reproducible loop for capability gains is a fundamentally different engineering posture.
The full video is worth watching for the specific steps in that feedback loop, not just the outcome. If the method generalizes beyond OpenClaw, it has implications for any agent development pipeline.
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