Cost or Coverage: Agent Orchestration under Access Scopes

As LLM-based multi-agent systems grow more complex, orchestration increasingly requires coordinating specialized agents while controlling resource use. When agents have overlapping access to information sources and different engagement costs, this becomes a joint inference-and-allocation problem: the orchestrator must infer which sources contain the required evidence and select a low-cost agent set that covers them. Whether current LLMs can coordinate these decisions remains unclear. To quantify the orchestrators' coordination capabilities, we compile a benchmark containing 178 two-source queries, a fixed team of eight access-scoped agents, two retrieval-grounded engagement-cost schemes, and oracle retrieval that isolates orchestration from retriever error. Across eight model configurations, first-attempt behavior separates into narrow commitment, which limits cost but frequently misses required sources, and broad hedging, which achieves higher coverage through substantial over-engagement. Revealing the required sources largely resolves the former but not the latter. In the revision round, outcome feedback facilitates correction, while cost feedback can induce more restrained routing but does not reliably reduce cost without sacrificing task success. Overall, models respond well to individual source or cost signals, but struggle to balance them when both coverage and cost must be optimized jointly.