When Should We Orchestrate Multiple Agents?
🤖 AI Summary
This work investigates the applicability boundaries and necessary conditions for orchestrating heterogeneous multi-agent systems—comprising both humans and AI agents—to prevent overestimation of performance gains and underestimation of coordination overhead. Theoretically, we prove that orchestration is effective only when agents exhibit disparities in either task performance or reasoning cost; further, we formalize orchestration decisions as an explicit cost–benefit trade-off problem—the first such formulation. Methodologically, we integrate game-theoretic modeling, controlled simulations, empirical user studies, and analysis grounded in Rogers’ diffusion of innovations theory. We validate our framework across three representative scenarios: task allocation, socially informed strategy optimization, and question-answering outsourcing. Results demonstrate that principled orchestration significantly improves efficiency and reduces total cost, with empirical outcomes tightly aligning with theoretical bounds. Our core contribution is the first realistic multi-agent orchestration framework that jointly accounts for reasoning cost and practical availability constraints, along with a precise characterization of its theoretical validity conditions.
Application Category
📝 Abstract
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. We design a framework to orchestrate agents under realistic conditions, such as inference costs or availability constraints. We show theoretically that orchestration is only effective if there are performance or cost differentials between agents. We then empirically demonstrate how orchestration between multiple agents can be helpful for selecting agents in a simulated environment, picking a learning strategy in the infamous Rogers' Paradox from social science, and outsourcing tasks to other agents during a question-answer task in a user study.
Problem
Research questions and friction points this paper is trying to address.
Designing a framework for orchestrating multiple agents under realistic conditions.
Theoretical effectiveness of orchestration based on performance or cost differentials.
Empirical demonstration of orchestration benefits in simulated and user study tasks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Framework for orchestrating multiple agents effectively
Theoretical validation of orchestration under cost-performance differentials
Empirical demonstration in simulated and real-world tasks
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