Wayfinder: Adaptive Resource Routing from Agent Citations

To act effectively, agents must seek information across heterogeneous applications including chat channels, issue trackers, email, knowledge bases, and code repositories, yet resource routing in such environments is inherently unknown to model providers: where relevant information resides varies across users and organizations and cannot be enumerated at training time. Existing routing approaches are typically trained offline under fixed assumptions or rely on static heuristics, limiting their ability to transfer across environments without target-specific supervision. We introduce \method, a routing policy trained via reinforcement learning from interaction feedback that enables adaptive resource selection while treating each resource as a black-box retriever. \method leverages an interaction-derived, citation-gated memory to prioritize candidate resources for downstream retrieval, while allowing the primary agent to expand its search when needed. We evaluate \method in controlled federated environments spanning both intra-corpus partitions (NFCorpus, FEVER) and cross-corpus heterogeneity (Tech Startup) and compare against supervised and zero-shot routing baselines. Interaction-driven adaptation outperforms all baselines on the intra-corpus benchmarks, where description-based discrimination is weak, and ties a fully supervised classifier on Tech Startup at matched top-$1$ output without using any target-environment labels.