Generic Multi-Agent AI Framework for Weighted Dynamic Corridor Price Optimisation

Abstract

The objective of this analysis is to address the challenges encountered by pricing
systems in managing real-time market dynamics. This study presents a fundamental
theoretical framework with a focus on taxonomy and ontology for a
domain-specific multi-agentic artificial intelligence (AI) serving as an internal
price advisor to optimise pricing strategies for products and services. The system
is designed to function in conjunction with other corporate AI systems and an
Enterprise Resource Planning System (ERP). The ERP serves as a high-quality
data foundation, and several other internal and external sources can provide
essential data with varying quality. Methods: The proposed AI model builds upon
the Weighted Dynamic Corridor Price Optimisation framework, which integrates
cost-plus and value-based pricing methodologies within a non-linear price corridor
bounded by lower and upper thresholds. In the context of supply chain integration,
fully-cooperative pricing models can apply Nash equilibrium to enhance
supply chain profitability, whilst semi-cooperative models mitigate information
asymmetry through the principal-agent theory. The findings from the theoretical
analysis of the generic industry- and product-agnostic multi-agentic AI system
suggest the system’s potential capacity for dynamically computing optimal prices.
A generative AI module could facilitate real-time decision-making, enabling sales
teams and similar stakeholders to simulate scenarios and refine pricing strategies.
In conclusion, the proposed AI system should be capable of delivering adaptive,
context-aware, and data-driven recommendations. Depending on its application,
the AI system could become very complex, susceptible to errors, and require
significant maintenance. Future research should focus on customising the proposed
AI system for specific industries and product categories and validating its
applicability through empirical research.