Sep 2026· The AI Magazine· Vol 47· 0 citations· 5 references
TL;DR
This work proposes a blockchain‐based backbone infrastructure that enables a verifiable trust layer for autonomous multi‐agent systems, integrating smart contracts, DIDs, and verifiable credentials (VCs), to ensure that trust is not assumed but earned through interactions.
Abstract
Agentic AI systems are increasingly capable of autonomous decision‐making and collaboration across distributed environments. However, existing frameworks largely assume trust among agents, without providing mechanisms for verifiable identity, behavioral accountability, or interaction transparency.
This work proposes a blockchain‐based backbone infrastructure that enables a verifiable trust layer for autonomous multi‐agent systems. Agent identities are bound to decentralized identifiers (DIDs) registered on‐chain, while an on‐chain Agent Registry enables reputation‐aware capability discovery. By integrating smart contracts, DIDs, and verifiable credentials (VCs), the framework ensures that trust is not assumed but earned through interactions: agents issue VC endorsements after each task, and dedicated smart contracts automatically update reputation scores, applying incentives and penalties accordingly. Blockchain provides immutability, transparency, and programmability, transforming trust from an implicit assumption into an explicit, auditable, and enforceable property of the multi‐agent ecosystem.
This survey and tutorial article reviews the literature over the period 1980--2026 on the evolution from classical multi-agent systems to open agent networks, with a particular focus on LLM-based autonomous agents, agent interoperability protocols, Internet-of-Agents infrastructures, and blockchain-enabled trust mechan...
Liehuang Zhu, Yu-Hang Li, Tianxing Wang et al.· 0 citations
A product- and vendor-neutral black-box architecture for agentic processes that creates blockchain-anchored cryptographic commitments for selected agent communications, human-in-the-loop approvals, tool calls, and process artifacts without placing sensitive content on-chain.
This paper proposes a blockchain-backed agentic security framework designed to safeguard the complete software development lifecycle (SDLC) while also securing the agentic AI components responsible for monitoring it. The framework coordinates a set of specialised security agents, covering source integrity, dependency a...
Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for agent reliability, participation, and system-level behavioral alignment. These shortcomings leave open environments severely vulnerable to uncooperative or malicious age...
This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...
Harsh Verma· International Journal of Sci...· 0 citations
Artificial Intelligence (AI) and Blockchain have emerged as two influential technologies in the development of modern
digital systems. AI provides intelligent prediction, automation, and decision-making capabilities, while Blockchain offers
decentralization, traceability, transparency, and resistance to unauthorized mo...
Akash Kanhai· International Journal for Re...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.