TeamBench: Evaluating Agent Coordination under Enforced Role Separation

2026-05-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

TeamBench is a new benchmark featuring 851 task templates and 931 seeded instances designed to evaluate agent coordination under operating system-enforced role separation. Unlike prompt-only systems, TeamBench uses sandboxes to restrict Planner, Executor, and Verifier roles, preventing any single role from accessing full requirements, modifying the workspace, and certifying the final answer simultaneously. Experiments reveal that while prompt-only and sandbox-enforced teams achieve statistically similar pass rates, prompt-only runs result in 3.6 times more instances of Verifiers attempting to edit Executor code. Verifiers frequently approve submissions that fail deterministic graders (49%), and their removal can improve mean partial scores. Team value is conditional, benefiting tasks where single agents struggle but hindering performance on easier tasks. A 40-session human study under similar role separation exposed distinct interaction patterns that pass rates alone would miss.

Key takeaway

For AI Architects and Research Scientists evaluating multi-agent systems, you should prioritize benchmarks that enforce role separation via access controls, not just prompts. Relying solely on pass rates can obscure critical insights into agent coordination failures, such as Verifiers approving faulty work or taking over other roles. Instead, integrate metrics like role-violation rates and stratify team performance by solo agent capability to understand when and how agent teams genuinely add value versus merely introducing overhead or new failure modes.

Key insights

Enforced role separation reveals true agent coordination and failure modes that prompt-only systems mask.

Principles

• Role separation requires information transfer.
• Team value is conditional on solo agent capability.
• Pass rate alone is insufficient for agent evaluation.

Method

TeamBench enforces role separation using OS permissions, with Planner, Executor, and Verifier roles in separate containers. Tasks are curated to require coordination, with deterministic graders and support for cross-provider role mixing.

In practice

• Use OS-level enforcement for true role separation.
• Stratify team value by solo agent performance.
• Monitor role violation rates from per-turn traces.

Topics

• TeamBench Benchmark
• Enforced Role Separation
• LLM Agent Coordination
• Verifier Reliability
• Conditional Team Performance

Code references

• ybkim95/TeamBench

Best for: AI Architect, AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.