arXiv:2610.22688v2 Announce Type: replace Abstract: Enterprise LLM agents using growing tool catalogs must surface capabilities as workflows evolve without flooding context or committing to the wrong tool. Adaptive retrieval can expose new capabilities, but when several tools seem plausible, probing them sequentially can stop at an early failure or leave failed attempts in the agent context. We present BRIA (BRanch-Inspect-Adopt), a plug-and-play method that investigates candidates in isolated branches. Each branch retrieves and executes tools independently. A task agent inspects the execution evidence and selectively adopts useful results in its context. BRIA also retrieves tools as new capability needs arise. To evaluate BRIA in an enterprise setting, we assemble a single catalog with 1897 tools from PlanBench-XL and MCP-Atlas. We compare BRIA with static and adaptive baselines using three LLM families. We observe that BRIA lies on the accuracy-cost frontier. Compared with the strongest baseline, BRIA uses less than half as many input tokens to achieve equal or higher success rates on PlanBench-XL, and improves task performance by up to 14.4 percentage points on MCP-Atlas. For enterprise agents over large catalogs, these results suggest that candidate tools can be effectively explored with execution evidence without carrying every result in the agent's context.