Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies

Abstract

Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling them to complete both routine and advanced tasks effectively. Despite its importance, existing relevant benchmarks largely evaluate agents on pre-specified or synthesized files with limited real-world dependencies, leaving workspace-level evaluation underexplored. To this end, we introduce Workspace-Bench, a benchmark for evaluating AI agents on Workspace Learning invOlving Large-Scale File Dependencies. We construct realistic workspaces with 5 worker profiles, 74 file types, 20,476 files (up to 20GB) and curate 388 tasks, each with its own file dependency graph, evaluated across 7,399 total rubrics that require cross-file retrieval, contextual reasoning, and adaptive decision-making. We further provide Workspace-Bench-Lite, a 100-task subset that preserves the benchmark distribution while reducing evaluation costs by about 70%. We evaluate 4 popular agent harnesses and 7 foundation models. Experimental results show that current agents remain far from reliable workspace learning, where the best reaches only 68.7%, substantially below the human result of 80.7%, and the average performance across agents is only 47.4%.

Figures (17)

• Figure 1: Performance on the lite version of Workspace-Bench. We evaluate four harnesses (ClaudeCode, DeepAgent, Hermes, and OpenClaw) with seven backbone LLMs. The top three configurations all pair Opus-4.7 with different harnesses, while lower-ranked combinations exhibit substantial variation across both harnesses and LLMs.
• Figure 2: Overview of Workspace-Bench.
• Figure 3: The data collection and curation pipeline of Workspace-Bench.
• Figure 4: Workspace (left) and task (right) distribution of the Workspace-Bench.
• Figure 5: An illustrative task example from Workspace-Bench.