With the rapid advancement of artificial intelligence technology, computational pathology has evolved from task-specific models to foundation models capable of supporting multiple organs, diseases, and downstream tasks. However, existing methods remain limited to single-pass inference on images and are unable to fully emulate the complex diagnostic processes encountered in real-world clinical settings. In recent years, large language models have driven the development of agent-based paradigms. By leveraging capabilities such as navigation, reasoning, and tool use, these frameworks offer a novel approach for modeling complex diagnostic workflows. Framed within the context of clinical pathology diagnostic processes, this article systematically reviews the key challenges and advances in computational pathology research across three stages: preliminary overview, diagnostic analysis, and report writing, and discusses the current functional roles and implementation approaches of agent-based technologies at each stage. Although validation scenarios in existing studies remain limited, the core modules and methods required to construct a pathology diagnostic agent system are largely in place. Agent-related technologies are expected to facilitate the development of a clinically deployable intelligent assistance platform for pathology diagnosis.

Pathology diagnosis is the "gold standard" for clinical disease diagnosis and treatment. Intelligent pathology image analysis has significant clinical value in improving diagnostic efficiency and ensuring consistency. This paper systematically reviews the core multiple instance learning paradigm for whole slide image analysis, and elaborates the pretraining system, core functional branches and latest advances of pathology foundation models. Taking clinical usability as the core evaluation dimension, it establishes a multi-dimensional evaluation framework, analyzes the clinical translation potential of different technical routes, and points out the core contradiction of "excellent laboratory performance but insufficient clinical usability" in current pathology foundation models. It further dissects the key clinical translation bottlenecks of pathology foundation models, outlines future application and development directions, and provides a reference for technological research and development as well as clinical translation in computational pathology.