Interactive External Knowledge Acquisition for Software Engineering Agents

Repository-level software engineering agents can inspect code and execute tests, yet useful evidence may lie outside their execution environment. Search and browsing tools expand access, but agents must decide when to retrieve, communicate local constraints, and assess external information's applicability. We present BRIDGE, a framework that separates repository-side problem solving from external evidence acquisition. A dedicated retriever provides exploratory and focused interfaces; during focused retrieval, it can request missing repository context and resume its search. An outcome-conditioned prompt-memory procedure helps a parameter-frozen agent use these interfaces, with memory constructed on a disjoint bootstrap set and fixed before evaluation. We evaluate this organization through local-only, direct-tools, and retriever-capacity comparisons. On 450 held-out SWE-bench Verified instances, BRIDGE with GPT-5-mini in both roles achieves $59.6\pm 1.37\%$, compared with $54.2\pm 1.21\%$ for direct access to the same web tools with the same adaptation (three-run mean $\pm$ standard deviation). Protocol and adaptation ablations, temporal-policy sensitivity experiments, and cost measurements characterize the design's behavior and computational trade-offs. These experiments show how coordinated evidence acquisition can complement repository-local reasoning.