Optimizing Agentic Workflows: From Task-Level Search to Dynamic Construction

Abstract

The rapid evolution of large language model (LLM) agents has demonstrated their immense potential in complex reasoning tasks, yet relying on manually designed agentic workflows fundamentally limits their adaptability and performance. In this talk, we present a trajectory of our recent research that progressively automates and refines workflow construction, moving from static to highly dynamic paradigms. We begin by introducing a task-level optimization framework that leverages bandit-guided graph evolution to discover a single, robust workflow capable of generalizing across all queries within a specific task domain. To push beyond the inherent performance ceiling of such task-level generalization, we then present a query-level orchestration method that utilizes contextual bandits to adaptively assign the optimal, customized workflow for every individual input. Finally, we address the limitations of one-shot, fixed workflow generation with Workflow-R1, a novel reinforcement learning-based approach for multi-turn workflow construction. Unlike previous methods where the workflow remains static after initial generation, Workflow-R1 optimizes group sub-sequence policies to dynamically construct and adjust the workflow online, conditioning subsequent steps directly on intermediate execution results. Together, these three works trace a clear path from static task-wide optimization to highly dynamic, execution-aware reasoning pathways, offering a comprehensive foundation for building more autonomous and resilient AI agents.

Biography

代忠祥博士现任香港中文大学（深圳）数据科学学院助理教授、博士生导师，入选国家级高层次青年人才，并获评校长青年学者。主持广东省自然科学基金优秀青年项目、国家自然科学基金青年科学基金项目（C类）、深圳市自然科学基金面上项目，以及华为大模型智能体合作项目等。于2024年在麻省理工学院（MIT）担任博士后研究员，并于2021年至2023年在新加坡国立大学（NUS）从事博士后研究。他于2015年和2021年分别获得新加坡国立大学的电气工程学士学位和计算机科学博士学位。研究兴趣涵盖机器学习的理论与应用。在应用层面，专注于大语言模型，研究方向包括基于大模型的智能体、大模型个性化、大模型在线路由、基于大模型的社会模拟、大模型提示优化等；在理论层面，深入研究多臂老虎机算法。已在顶级AI会议和期刊上发表论文38余篇，其中30余篇发表于NeurIPS、ICML和ICLR，并担任NeurIPS、ICML和ICLR的领域主席（Area Chair）。