Securing Agentic AI Systems through Blockchain: A Comprehensive Review of Trust, Autonomy, and Decentralized Frameworks

Abstract

Agentic Artificial Intelligence (AI) systems—those capable of autonomous planning, negotiation, and adaptive goal pursuit—are rapidly moving from experimental prototypes into operational infrastructures. While their decision-making capabilities have advanced significantly, mechanisms for ensuring accountability, trust, and security have not matured at the same pace. This imbalance introduces systemic vulnerabilities, particularly in distributed environments where agents interact without centralized supervision. Blockchain technology has frequently been proposed as a structural solution to these challenges. However, its practical suitability for securing agentic AI remains debated.

This review does not assume blockchain as an inherent remedy. Instead, it critically examines where decentralized ledgers meaningfully enhance agent autonomy and where they introduce new technical and governance complexities. By synthesizing current research, early empirical deployments, and architectural case analyses, this paper argues that blockchain can serve as a trust augmentation layer rather than a universal security mechanism. Scalability, privacy compliance, and incentive alignment remain unresolved challenges. The findings suggest that future secure agentic AI systems will likely depend on hybrid governance architectures rather than purely decentralized or purely centralized models