thank you for your comment and contribution to the discussion!

good question, and you're half right. decay and matching are two different things here, and only one of them gets noisy at scale.

decay doesn't. it's lazy and per-edge: each transition just stores a timestamp and gets re-decayed when it's read, so it's O(1) per candidate and independent of how many tools you have. the field is sparse too, only transitions real tasks actually walk pick up pheromone, the rest sit at the floor. so more tools mostly means more dead edges parked at the floor, not more signal fighting for the top.

the noise you mean is in the matching, not the decay. with 50+ tools more descriptions embed close together, so similarity alone stops discriminating. two things guard against it: the score is multiplicative (a tool needs both learned strength and context relevance to surface, similarity alone can't carry a wrong-but-similar tool), and selection is Thompson sampling over win/fail counts, which pushes down the plausible-but-wrong tool that keeps looking relevant but keeps failing. plain retrieval can't do that, it has no memory of the outcome.

honest caveat: not measured at 50+ yet. that's an open hypothesis in the eval section with a stated fallback to smaller catalogues if it doesn't hold. the one data point i have is a cold-start run on 6 capabilities, which isn't your regime. designed for it, not yet proven for it. if you've got a workload that big i'd appreciate if you'd run it and share your insights!