Teaching an Agent to Change Its Mind
A memory architecture for autonomous search: how an agent explores, backtracks, and stays coherent over dozens of iterations without losing the thread.
If you’ve spent any afternoon on audience segmentation, you know the shape of it. Cluster, squint at the silhouette scores, adjust k, swap a feature, rerun, repeat. By the time you have something presentable, you’ve spent two days on manual trial and error with better vocabulary.
That loop isn’t specific to segmentation. Any time a problem is a search too large to explore by hand, and “good” resists a single number, you end up doing this: try, judge, adjust, repeat.
The real question is: can an agent run the same loop (propose, judge, backtrack) on its own?
I believe the answer is yes. But the interesting part was never the clustering. It was the architecture that lets an agent explore autonomously without drowning in its own history.
The Shape of a Problem Like This
Strip away the specifics: a raw data export and a stakeholder who wants a handful of distinct, actionable groups. On the surface, a clustering job. Underneath, “good” has to clear two bars: statistically real (coheres in…
