Where to Think, What to Explore: Hierarchical Agentic Reasoning and Exploration for Coding Agents

Coding agents work by interacting with a repository over many turns, so the compute they spend at inference time is as much a design choice as the model itself. That compute can go into reasoning inside a decision, more turns, more candidate actions, or parallel subagents, and these choices have not been compared under one accounting. We measure the four dimensions (reasoning depth $D$, interaction horizon $I$, action width $W$, subagent fan-out $S$) and their interactions under one coding agent, model, benchmark, serving stack, and cost accounting. Scaled alone they saturate for different reasons: depth has a finite operating point, horizon reaches diminishing returns, width produces candidate diversity that naive selection fails to exploit, and delegation uptake limits passive subagents. They also interact: reasoning changes both the value of later interaction and the cost of parallel branching, and the depth of the step that reduces a fan-out to one action sets what that fan-out is worth. These measurements motivate HARE, Hierarchical Agentic Reasoning and Exploration, which treats $D$ and $I$ as a serial pair and $W$ and $S$ as a parallel pair. HARE reasons only at the turns that write to the repository or synthesize gathered evidence, and folds each fan-out back into one committed action with a single reduction step. On SWE-bench-Pro tasks with a Qwen3.8-27B model, HARE matches dense reasoning at 15% less wall-clock and raises the best measured resolve rate from 56.7 to 60.0%. Whole-trajectory sampling and sequential refinement stay below the frontier HARE reaches, at higher cost.