Insured Agents: A Decentralized Trust Insurance Mechanism for Agentic Economy

2025-12-09

AI Summary

In open agent networks, LLM-based agents suffer from trustworthiness deficiencies—including hallucination, prompt injection, and tool misuse—while existing protocols lack effective trust mechanisms. Method: We propose a decentralized trust insurance framework that introduces specialized insurance agents to provide TEE-protected staking guarantees and auditing rights for operational agents; we further design a hierarchical underwriting market integrating slashable collateral, on-chain evidence adjudication, and model-drift-resilient dispute resolution. Contribution: This work pioneers the integration of insurance economics into LLM agent governance, enabling economically incentive-compatible trustworthy collaboration. It significantly enhances security and robustness of cross-network, heterogeneous agent interactions, and supports scalable deployment.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The emerging "agentic web" envisions large populations of autonomous agents coordinating, transacting, and delegating across open networks. Yet many agent communication and commerce protocols treat agents as low-cost identities, despite the empirical reality that LLM agents remain unreliable, hallucinated, manipulable, and vulnerable to prompt-injection and tool-abuse. A natural response is "agents-at-stake": binding economically meaningful, slashable collateral to persistent identities and adjudicating misbehavior with verifiable evidence. However, heterogeneous tasks make universal verification brittle and centralization-prone, while traditional reputation struggles under rapid model drift and opaque internal states. We propose a protocol-native alternative: insured agents. Specialized insurer agents post stake on behalf of operational agents in exchange for premiums, and receive privileged, privacy-preserving audit access via TEEs to assess claims. A hierarchical insurer market calibrates stake through pricing, decentralizes verification via competitive underwriting, and yields incentive-compatible dispute resolution.

Problem

• Addresses unreliability and security vulnerabilities in autonomous agent systems.
• Proposes decentralized trust insurance to manage heterogeneous task verification challenges.
• Introduces a market-based mechanism for incentive-compatible dispute resolution.

Innovation

• Decentralized insurer agents posting stake for operational agents
• Privacy-preserving audit access via TEEs for claim assessment
• Hierarchical insurer market calibrating stake through competitive pricing