EntCollabBench benchmarks enterprise multi-agent AI collaboration

A new research paper titled "Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows" introduces EntCollabBench, a benchmark designed to evaluate how well LLM agents handle real-world enterprise collaboration. Unlike existing benchmarks that focus on single agents with broad tool access or multi-agent setups without realistic constraints, EntCollabBench simulates a permission-isolated organization with 11 role-specialized agents across six departments. It includes two evaluation subsets: a Workflow subset where agents collaboratively modify enterprise system states, and an Approval subset where they make policy-grounded decisions. The benchmark uses execution traces, database state verification, and deterministic policy adjudication rather than relying on natural-language response judging, making results highly reproducible.

Experiments with representative LLM agents show that current models still struggle significantly with end-to-end enterprise collaboration. Key failure points include delegation (assigning tasks to the right agent), context transfer (passing information between agents), parameter grounding (correctly interpreting task parameters), workflow closure (completing multi-step processes), and decision commitment (following through on approvals). EntCollabBench provides a much-needed testbed for measuring and improving agent systems intended for realistic organizational environments, highlighting the gap between all-in-one AI agents and the specialized, role-based collaboration required in enterprises.