Does Multi-Agent Collaboration Pay When Agents Serve Users? Testing Agent Scaling in Dual-Control Settings

Ethan Yang, Xiangcheng Shi

Multi-agent systems (MAS) built from language models are deployed on the premise that collaboration beats a single-agent system (SAS), yet when it pays is unclear. The most systematic study to date gives a capability-saturation rule: coordination helps while the SAS is weak and hurts once it is strong, with the crossing near 45% single-agent accuracy. That rule was derived on autonomous benchmarks, where no user takes part. We test it where agents now work: in conversation with a user under a written policy, on $τ^2$-bench, the dual-control benchmark. On its airline, retail and telecom domains we compare the stock SAS with a centralized MAS built from the same model for six models from three families, over about 7,950 simulated conversations, with one completion-token ledger per episode per system. The study was pre-registered with a public change log; the MAS is reported as built, with one validity bar unmet (its planner never saw the tool schemas). Under a fixed simulated user no cell shows a MAS benefit: the MAS is worse in 12 of the 13 valid cells and tied in the thirteenth, and the fitted gain is negative across the observed capability range (weighted mean -28 points, 95% CI…