Pricing Models for Agentic AI Services: A Multi-Sided Market Analysis

We examine pricing strategies for agentic artificial intelligence (AI) services through the lens of multi-sided markets and game theory. Our analysis reveals that traditional software pricing models inadequately capture the autonomous, outcome-driven value proposition of agentic AI. We propose a framework combining value-based pricing with multi-sided market dynamics, supported by empirical evidence from enterprise software and e-commerce implementations. This research contributes to understanding how organizations can effectively price and adopt AI agents that operate with minimal human supervision while delivering measurable business outcomes