AI research preference model
In the practice of Agentic AI in 2026, research preference models are becoming a new orchestration primitive. This model aims to optimize the collaboration process of agents by clearly defining task objectives, constraints, and evaluation criteria, addressing the issue of lack of unified decision-making logic in traditional automation systems. Currently, several technical teams have integrated this mechanism into core workflows to enhance the stability and interpretability of complex tasks. In the future, with the improvement of the data feedback loop, research preference models will further evolve into a key interface connecting underlying algorithms with upper-layer applications, driving Agentic AI toward transformation from single-functional modules to autonomous collaborative systems.