Research: coding agents choose skill granularity at runtime with fallback to primitive tool calls

Nice work on using skills more effectively for coding agents. (bookmark it) In this work, researchers treat skill granularity as a runtime decision. For each task, the agent can use primitive tool calls, a macro skill, or a macro skill that falls back to primitive steps when it fails. Fixed macro execution is efficient when the skill fits and brittle when it does not. The controller picks the level from task structure, execution state, and earlier macro failures, and a finite-state executor handles the fallback as an explicit transition instead of an informal retry. In a 12-task routing study, a learned router reaches 100% success where a hand-written heuristic fails. With macro-to-micro fallback, the agent recovers controlled macro failures that fixed granularity leaves unresolved. arxiv.org/abs/2610.31461 Chat with Paper: academy.dair.ai/papers/adaptive…