Orchestration Governance Frameworks For Agentic Supply Chains: Resolving Agent Conflicts Under Uncertainty

Abstract

Supply chain management can dramatically change as autonomous artificial intelligence agents with specialized decision-making abilities are truly deployed across forecasting, procurement, inventory optimization, logistics, and sustainability. These intelligent systems show a large increase in their operation; they are much more accurate, responsive, and effective. However, autonomous agent deployment introduces critical governance challenges when multiple agents propose conflicting actions based on divergent optimization objectives. Traditional centralized control mechanisms with static rule hierarchies prove inadequate for managing adaptive, probabilistic agent behaviors operating under uncertainty. The Orchestration Governance Framework addresses these challenges through a systematic three-layer architecture integrating operational agents, coordination mechanisms, and governance enforcement. The framework employs the Belief-Desire-Intention cognitive model, enabling agents to manage complex decision spaces while maintaining computational tractability. Conflict resolution operates through a structured four-stage pipeline combining explicit rule-based detection, statistical variance measurements, multi-objective optimization exploring Pareto-efficient solutions, and dynamic weight tuning aligned with organizational priorities. Probabilistic modeling accommodates inherent supply chain uncertainty through Bayesian inference. Validation through consumer goods manufacturing demonstrates successful resolution of demand growth versus emission constraint conflicts, achieving substantial revenue capture while maintaining environmental compliance. The framework preserves agent autonomy and continuous learning capabilities while ensuring regulatory adherence and stakeholder trust. Implementation considerations address data infrastructure dependencies, computational complexity scaling, and multi-agent learning stability requirements essential for enterprise deployment.