BrainAgent-CGP: Constraint-Guided Hierarchical Planning for Reliable Autonomous Brain-Signal Analysis

Electroencephalography (EEG) analysis often relies on expert-designed pipelines in which processing steps, tool order, and intermediate data are defined manually. Large language model (LLM)-based agents can automate parts of this process from natural-language requests, but their generated workflows may still be invalid when required preprocessing steps or intermediate artifacts are missing. We propose BrainAgent-CGP, a constraint-guided multi-agent framework that separates semantic goal interpretation from executable workflow construction. Each analytical tool declares its required and produced artifacts, supported goals, agent ownership, execution cost, and reliability. A uniform-cost planner uses this information to construct dependency-valid workflows, followed by deterministic agent routing, runtime validation, and state-preserving replanning. BrainAgent-CGP is evaluated on 30 natural-language tasks spanning low, medium, and high workflow complexity. It achieves a Planning Success rate, Plan Validity Rate, Required-Tool Recall, and routing accuracy of 1.00, with an Invalid Tool Rate of 0.00. Removing artifact constraints reduces Plan Validity Rate to 0.233 and increases Invalid Tool Rate to 0.273. A language-robustness study over 90 paraphrased and unseen requests achieves an overall goal exact-match rate of 0.989 with the hybrid semantic interpreter while maintaining a Plan Validity Rate of 1.00. Controlled failure experiments achieve a Recovery Rate of 1.00 when an alternative path is available and a Safe Deferral Rate of 1.00 when no valid alternative exists. Finally, BrainAgent-CGP successfully executes 48 tool operations across 12 ISRUC-Sleep and Sleep-EDF Expanded recordings totaling 177.41 hours of EEG/PSG data. These results show that explicit artifact-state planning can improve the reliability of autonomous brain-signal analysis.