Equity-Aware Agentic AI Architectures and Governance With SDG-Aligned Healthcare Innovation

Abstract

Agentic AI marks a shift from static forecasting to autonomous, goal-oriented systems for healthcare workflows. Unlike conventional AI models, agentic AI integrates adaptive learning with planning, execution, monitoring, and coordination within multi-agent architectures. This chapter explores how these systems collaborate with UN SDGs 3, 9, 10, and 17—directly related to health equity. The chapter formally models the proposed system as a Partially Observable Markov Decision Process (POMDP), introducing stochastic foundations including transition dynamics, belief updating, and Bellman optimality for policy learning under uncertainty. Probabilistic fairness metrics evaluate disparities using confidence intervals. Uncertainty quantification addresses aleatoric and epistemic uncertainty via risk-aware optimization. The suggested Equity-Aware Agent Architecture integrates governance, federated learning, fairness constraints, and human oversight. Combining subgroup equity data with clinical metrics enables agentic AI to reduce inequities while maintaining efficiency.