A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines
This addresses trust and integrity problems for applications in healthcare, smart cities, and supply chain management, though it appears incremental as it combines existing technologies like LangChain and blockchain.
The paper tackles the trust and oversight issues in agentic AI systems by proposing a blockchain-monitored architecture, achieving efficient prevention of unauthorized practices and maintaining operational latency within reasonable ranges.
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.