CATC: Coordinating Clinical Agent Team via Runtime Local–Global Trace Audit

Clinical large language model agent teams coordinate specialized roles to solve complex clinical tasks. Existing approaches primarily verify individual responses or final outputs, leaving failures that emerge and propagate through inter-agent communication and context management unobserved during execution. We introduce Coordinating Clinical Agent Team (CATC), a framework for coordinating a clinical agent team via runtime local–global trace audit. CATC follows a two-stage design. An independent auditor combines dynamically generated code checks with semantic reasoning over an incremental interaction graph to identify local agent failures and global inconsistencies in communication and context propagation. CATC then converts structured audit findings into targeted corrections by the agent team, while an independent verifier controls whether each correction is accepted. We evaluate CATC across multiple clinical datasets spanning perception-oriented and cognition-oriented tasks, examining audit accuracy, failure localization, final task performance, intervention safety, and computational cost. These results establish runtime trace audit as a mechanism for converting additional agent-team computation into more reliable clinical reasoning.