PACT: A Contract-Theoretic Framework for Pricing Agentic AI Services Powered by Large Language Models

TL;DR

This paper introduces PACT, a contract-theoretic framework for pricing agentic AI services powered by large language models, addressing challenges in cost, QoS, and liability to improve service alignment and scalability.

Contribution

It presents a novel contract-theoretic pricing model for agentic AI services that considers multi-dimensional QoS and liability, ensuring incentive compatibility and individual rationality.

Findings

PACT improves QoS alignment between users and providers.

The framework accounts for costs, QoS, and liability in pricing.

Numerical evaluations demonstrate scalability and effectiveness.

Abstract

Agentic AI, often powered by large language models (LLMs), is becoming increasingly popular and adopted to support autonomous reasoning, decision-making, and task execution across various domains. While agentic AI holds great promise, its deployment as services for easy access raises critical challenges in pricing, due to high infrastructure and computation costs, multi-dimensional and task-dependent Quality of Service (QoS), and growing concerns around liability in high-stakes applications. In this work, we propose PACT, a Pricing framework for cloud-based Agentic AI services through a Contract-Theoretic approach, which models QoS along both objective (e.g., response time) and subjective (e.g., user satisfaction) dimensions. PACT accounts for computational, infrastructure, and potential liability costs for the service provider, while ensuring incentive compatibility and individual rationality for the user under information asymmetry. Through contract-based selection, users receive tailored service offerings aligned with their needs. Numerical evaluations demonstrate that PACT improves QoS alignment between users and providers and offers a scalable, liable approach to pricing agentic AI services in the future.