A SECURE DECENTRALIZED TRUST FRAMEWORK FOR AUTONOMOUS AI AGENTS USING BLOCKCHAIN, SMART CONTRACTS, AND ADAPTIVE REPUTATION LEARNING

Abstract

Autonomous Artificial Intelligence (AI) agents have become a revolutionary force in distributed computing, enabling intelligent systems to independently perceive environments, make decisions, negotiate services, and collaborate without constant human intervention. Autonomous AI agents are increasingly being deployed in healthcare, finance, industrial automation, intelligent transportation, smart cities, decentralized finance (DeFi), and Internet of Things (IoT) ecosystems where secure and trustworthy interactions are of paramount importance. However, the existing trust management mechanisms mainly depend on centralized architectures that suffer from single points of failures, limited scalability, privacy concerns, and vulnerability to insider attacks. Blockchain technology provides a decentralized, immutable and transparent recording of transactions. But most of the blockchain-based trust management systems are constructed based on static reputation mechanisms which are not sufficient to describe the behavioral changes of autonomous AI agents in the course of time. Moreover, current frameworks rarely integrate adaptive trust learning and smart contract based policy enforcement which restricts their applicability in dynamic decentralized environments. We propose TrustChain-AI, a secure decentralized trust platform based on blockchain, smart contracts and adaptive reputation learning, for trustworthy interactions between autonomous AI agents. This paper proposes a framework which employs a permissioned blockchain to store immutable trust records and smart contracts to automate the processes of identity verification, transaction validation, policy enforcement, and trust updates. A dynamic reputation engine continually observes agent behavior based on interaction history, service reliability, peer feedback and behavioral consistency, and dynamically computes trust scores to detect malicious agents. Different from the traditional methods, TrustChain-AI combines the decentralized trust verification and the intelligent behavior assessment. The real-time trust adapting is allowed without the centralized supervision. Its aim is to give a framework for increased transparency, scaleability and resilience against identity spoofing, Sybil attacks, replay attacks and reputation manipulation. We present a reproducible experimental setup using a permissioned blockchain network and simulated autonomous AI agents to evaluate the prediction accuracy of trust, transaction throughput, consensus latency, detection of malicious agents and computational overhead. The comparative analysis demonstrates the effectiveness of the proposed framework in providing secure, scalable and adaptive trust management for next generation decentralized AI ecosystems.