A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines

TL;DR

This paper proposes a blockchain-monitored agentic AI architecture that enhances trust, security, and transparency in autonomous decision-making systems across various domains.

Contribution

It introduces a novel architecture combining LangChain multi-agent systems with permissioned blockchain for monitoring, policy enforcement, and auditability.

Findings

Blockchain verification prevents unauthorized actions.

Ensures traceability of decision-making processes.

Maintains operational latency within acceptable limits.

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.