LLM-based agents are rapidly being plugged into expert decision-support, yet in messy, high-stakes settings they rarely make the team smarter: human-AI teams often underperform the best individual, experts oscillate between verification loops and over-reliance, and the promised complementarity does not materialise. We argue this is not just a matter of accuracy, but a fundamental gap in how we conceive AI assistance: expert decisions are made through collaborative cognitive processes where mental models, goals, and constraints are continually co-constructed, tested, and revised between human and AI. We propose Collaborative Causal Sensemaking (CCS) as a research agenda and organizing framework for decision-support agents: systems designed as partners in cognitive work, maintaining evolving models of how particular experts reason, helping articulate and revise goals, co-constructing and stress-testing causal hypotheses, and learning from the outcomes of joint decisions so that both human and agent improve over time. We sketch challenges around training ecologies that make collaborative thinking instrumentally valuable, representations and interaction protocols for co-authored models, and evaluation centred on trust and complementarity. These directions can reframe MAS research around agents that participate in collaborative sensemaking and act as AI teammates that think with their human partners.

翻译：基于大语言模型的智能体正迅速融入专家决策支持系统，但在复杂且高风险的环境中，它们很少能使团队更智能：人机团队的表现往往逊于最优个体，专家在验证循环与过度依赖之间摇摆，预期的互补性未能实现。我们认为这不仅关乎准确性，更反映了当前AI辅助设计理念的根本性缺失：专家决策是通过协作式认知过程实现的，其中人类与AI持续共同构建、检验并修正心智模型、目标与约束。我们提出“协作式因果意义建构”作为决策支持智能体的研究议程与组织框架：这类系统被设计为认知工作的合作伙伴，能够维护特定专家推理方式的动态模型，协助阐明与修正目标，共同构建并压力测试因果假设，并从联合决策的结果中学习，使人类与智能体随时间共同提升。我们概述了关键挑战：构建使协作思维具有工具性价值的训练生态、支持协同建模的表征与交互协议，以及以信任与互补性为核心的评价体系。这些方向可将多智能体系统研究重新聚焦于参与协作意义建构的智能体，使其成为与人类伙伴共同思考的AI队友。