From identifying missed HCCs to validating encounters before submission, AgentHDM agents turn fragmented workflows into a single, intelligent operating layer — reducing rework, improving accuracy, and strengthening compliance.

Each AgentHDM agent is engineered as a purpose-specific decision system — not a generic AI assistant. While agents share a common intelligence layer, their capabilities are tightly scoped to the function they perform, ensuring consistent behavior across analytics, chart review, and submission workflows.

Agents combine deterministic logic (CMS guidance, HCC hierarchies, and submission rules) with statistical models trained on historical claims, encounters, and chart data. This hybrid approach allows the system to produce explainable outputs — showing what was identified, how it was evaluated, and which evidence supports the result.

Rather than acting autonomously on source systems, agents generate structured, traceable outputs designed for downstream use by analysts, coders, and operations teams — enabling review, validation, and confident execution at scale.