Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents
This addresses the problem of measuring autonomous action in AI agents for researchers, highlighting current limitations and future directions, though it is incremental as it builds on existing evaluation frameworks.
The paper tackled the challenge of evaluating proactive problem-solving in LLM agents by introducing PROBE, a benchmark that decomposes proactivity into three capabilities, and found that even state-of-the-art models like GPT-5 and Claude Opus-4.1 achieve only 40% end-to-end performance.
LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and solve them autonomously. However, evaluating proactivity is challenging; current benchmarks are constrained to localized context, limiting their ability to test reasoning across sources and longer time horizons. To address this gap, we present PROBE (Proactive Resolution Of BottlEnecks). PROBE decomposes proactivity as a pipeline of three core capabilities: (1) searching for unspecified issues, (2) identifying specific bottlenecks, and (3) executing appropriate resolutions. We apply PROBE to evaluate leading LLMs and popular agentic frameworks, showing that even state-of-the-art models struggle to solve this benchmark. Computing our consistent measurements across frontier LLMs and agents, we find that the best end-to-end performance of 40% is achieved by both GPT-5 and Claude Opus-4.1. Additionally, we demonstrate the relative capabilities of each model and analyze mutual failure modes. Our results highlight the current limitations of autonomous action in agentic systems, and expose promising future research directions.