让AI当团队经理，协调人机协作的复杂任务
Orchestrating Human-AI Teams: The Manager Agent as a Unifying Research Challenge
- 设计能分解任务、分配工作、动态调整的自主管理代理
- 多任务协同中难以同时兼顾目标完成、约束遵守和执行效率
- 适合研究人机协作、智能体系统与自动化管理的学者
尽管智能体AI已能独立完成单项任务，但管理复杂的多智能体工作流仍是难题。本文提出一种研究愿景：构建能协调动态人机协作团队的自主管理智能体。该智能体需将复杂目标分解为任务图，分配给人类与AI成员，监控进度，适应环境变化，并保持透明沟通。我们将其建模为部分可观测随机博弈，识别出四大基础挑战：（1）分层分解的组合推理，（2）偏好变化下的多目标优化，（3）临时团队中的协调与规划，（4）内置治理与合规机制。为推进此研究，我们发布MA-Gym——一个开源的多智能体工作流编排仿真与评估框架。在20个工作流上评估基于GPT-5的管理代理，发现其难以同时优化目标达成率、约束遵守率和运行时间，凸显工作流管理仍是开放难题。最后讨论了自主管理系统带来的组织与伦理影响。
原文摘要 · Abstract (English)
While agentic AI has advanced in automating individual tasks, managing complex multi-agent workflows remains a challenging problem. This paper presents a research vision for autonomous agentic systems that orchestrate collaboration within dynamic human-AI teams. We propose the Autonomous Manager Agent as a core challenge: an agent that decomposes complex goals into task graphs, allocates tasks to human and AI workers, monitors progress, adapts to changing conditions, and maintains transparent stakeholder communication. We formalize workflow management as a Partially Observable Stochastic Game and identify four foundational challenges: (1) compositional reasoning for hierarchical decomposition, (2) multi-objective optimization under shifting preferences, (3) coordination and planning in ad hoc teams, and (4) governance and compliance by design. To advance this agenda, we release MA-Gym, an open-source simulation and evaluation framework for multi-agent workflow orchestration. Evaluating GPT-5-based Manager Agents across 20 workflows, we find they struggle to jointly optimize for goal completion, constraint adherence, and workflow runtime - underscoring workflow management as a difficult open problem. We conclude with organizational and ethical implications of autonomous management systems.
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