Trust-as-a-Service: Task-Specific Orchestration for Effective Task Completion via Model Context Protocol-Aided Agentic AI

TL;DR

This paper introduces Trust-as-a-Service (TaaS), a system that enables efficient, task-specific trust evaluation and collaboration among distributed devices using an agentic AI framework and Model Context Protocol.

Contribution

It proposes a novel TaaS paradigm that unifies trust mechanisms and leverages agentic AI with MCP for dynamic, need-driven trust assessment and collaboration.

Findings

Achieves 100% collaborator selection accuracy.

Ensures high reliability in task completion.

Demonstrates resource-efficient operations.

Abstract

As future tasks in networked systems are increasingly relying on collaborative execution among distributed devices, trust has become an essential tool for securing both reliable collaborators and task-specific resources. However, the diverse requirements of different tasks, the limited information of task owners on others, and the complex relationships among networked devices pose significant challenges to achieving timely and accurate trust evaluation of potential collaborators for meeting task-specific needs. To address these challenges, this paper proposes Trust-as-a-Service (TaaS), a novel paradigm that encapsulates complex trust mechanisms into a unified, system-wide service. This paradigm enables efficient utilization of distributed trust-related data, need-driven trust evaluation service provision, and task-specific collaborator organization. To realize TaaS, we develop an agentic AI-based framework as the enabling platform by leveraging the Model Context Protocol (MCP). The central server-side agent autonomously performs trust-related operations in accordance with specific task requirements, delivering the trust assessment service to all task owners through a unified interface. Meanwhile, all device-side agents expose their capabilities and resources via MCP servers, allowing devices to be dynamically discovered, evaluated, engaged, and released, thereby forming task-specific collaborative units. Experimental results demonstrate that the proposed TaaS achieves 100\% collaborator selection accuracy, along with high reliability and resource-efficient task completion.