OrchBench: Evaluating Multi-Agent Orchestration Plans in Isolation via Deterministic Simulation

2026-07-28

AI Summary

Existing evaluation methods struggle to disentangle the quality of task orchestration in multi-agent systems from confounding factors such as agent capabilities and environmental noise, while real-world execution incurs prohibitive costs. To address this, this work proposes OrchBench—a deterministic simulation-based benchmarking platform that models task dependencies via directed acyclic graphs and enables isolated, efficient assessment of orchestration plans. OrchBench achieves the first interpretable evaluation of orchestration quality with dramatically reduced overhead: requiring only 1.3% of the tokens and 10.3% of the time compared to real execution, while maintaining high fidelity (Pearson r = 0.816). Furthermore, it reveals that information retention rate is more critical to performance than simply increasing the number of agents.

Abstract

Complex tasks often decompose into parallelizable yet interdependent subtasks, making orchestration critical to the performance of multi-agent systems (MAS). Existing evaluations typically rely on end-to-end execution, which conflates orchestration-plan quality with worker capabilities, tool reliability, and environmental noise. Moreover, the time and token costs of real execution grow rapidly with workflow scale, making systematic evaluation expensive. We present OrchBench, a simulation-based benchmark for evaluating multi-agent orchestration plans in isolation. Starting from real-world tasks, OrchBench constructs directed acyclic graphs (DAGs) that encode task dependencies, with controlled sizes and degrees of parallelism. Given a DAG, a per-agent context limit, and an agent budget, the evaluated planner assigns subtasks to agents and specifies cross-agent information transfers and their retention ratios. A deterministic simulator evaluates the resulting plan without invoking worker agents and returns interpretable measures of result quality, makespan, and token cost. The simulated scores produced by OrchBench correlate strongly with quality scores from Claude Code executions, achieving a Pearson correlation of \(r=0.816\), while requiring only \(1.3\%\) of the tokens and \(10.3\%\) of the wall-clock time. Across diverse planners and workflow scales, we find that preserving task-critical information is more important than simply increasing the number of agents, and the benefits of parallelism diminish as coordination failures accumulate. These results establish OrchBench as an efficient and interpretable benchmark for comparing and diagnosing multi-agent orchestration plans.

Problem

• multi-agent orchestration
• evaluation benchmark
• deterministic simulation
• task decomposition
• coordination overhead

Innovation

• multi-agent orchestration
• deterministic simulation
• task dependency graph
• benchmarking
• scalable evaluation