A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines
Salman Jan, Hassan Ali Razzaqi, Ali Akarma, Mohammad Riyaz Belgaum
TL;DR
The paper addresses the need for trustworthy autonomous agentic AI by embedding auditable perception–reasoning–action loops within a permissioned blockchain. It proposes a four-layer architecture that combines LangChain-based multi-agent reasoning with smart-contract-driven governance on Hyperledger Fabric, and an MCP-based action layer to execute approved decisions. The authors implement and evaluate the framework on health monitoring, inventory management, and traffic control scenarios, showing strong traceability, effective policy enforcement, and only modest latency overhead due to blockchain integration. The results demonstrate that blockchain-backed governance can prevent unsafe autonomous actions while maintaining real-time performance, offering a practical blueprint for secure, auditable, high-impact agentic AI in critical domains. Future work highlights ledger scalability, cross-chain interoperability, and integration with on-chain risk scoring to further enhance governance in large-scale deployments.
Abstract
The application of agentic AI systems in autonomous decision-making is growing in the areas of healthcare, smart cities, digital forensics, and supply chain management. Even though these systems are flexible and offer real-time reasoning, they also raise concerns of trust and oversight, and integrity of the information and activities upon which they are founded. The paper suggests a single architecture model comprising of LangChain-based multi-agent system with a permissioned blockchain to guarantee constant monitoring, policy enforcement, and immutable auditability of agentic action. The framework relates the perception conceptualization-action cycle to a blockchain layer of governance that verifies the inputs, evaluates recommended actions, and documents the outcomes of the execution. A Hyperledger Fabric-based system, action executors MCP-integrated, and LangChain agent are introduced and experiments of smart inventory management, traffic-signal control, and healthcare monitoring are done. The results suggest that blockchain-security verification is efficient in preventing unauthorized practices, offers traceability throughout the whole decision-making process, and maintains operational latency within reasonable ranges. The suggested framework provides a universal system of implementing high-impact agentic AI applications that are autonomous yet responsible.