Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants
Researchers propose the Pare framework, which models applications as finite state machines to enable realistic user simulation for proactive agents, and release Pare-Bench with 143 tasks.
content type paperpublished July 2026
Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants
AuthorsDeepak Nathani†, Cheng Zhang‡, Chang Huan†, Jiaming Shan†, Yinfei Yang**, Alkesh Patel, Zhe Gan, William Yang Wang†, Michael Saxon§, Xin Eric Wang†
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Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequential nature of user interaction in digital environments and making realistic user simulation infeasible. We introduce Proactive Agent Research Environment (Pare), a framework for building and evaluating proactive agents in digital environments. Pare models applications as finite state machines with stateful navigation and state-dependent action space for the user simulator, enabling active user simulation. Building on this foundation, we present Pare-Bench, a benchmark of 143 diverse tasks spanning communication, productivity, scheduling, and lifestyle apps, designed to test context observation, goal inference, intervention timing, and multi-app orchestration.
† University of California, Santa Barbara
‡ Independent Researcher
§ University of Washington
** Work done while at Apple
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