Blockchain Empowered Trustworthy Agent Networks: Foundations, Taxonomy, and Future Directions

Enabling trustworthy agentic AI through blockchain technology

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.

Skillsets on the Chain: A Blockchain-Based Zero-Trust Framework for Agentic AI Networking

Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design.

DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration

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 agents. This work proposes DART, a Directed Acyclic Graph (DAG)-based reputation and incentive regulation framework for trustworthy multi-agent collaboration, combining centralized operational orchestration with blockchain-enabled decentralized governance and accountability. DART unifies DAG workflow orchestration, capability and reputation-aware task allocation, dynamic behavior updates, multi-factor incentives, and smart contract accountability paired with IPFS storage. Under this paradigm, agent selection dynamically balances task alignment, historical reputation, and workload, while post-execution behavioral evidence continuously calibrates agent trust and the probability of future participation. Evaluated across four axes, DART achieves 93.6% Pass@1 on GSM8K and builds a full-stack application in 142 s using two agents, outperforming centralized baselines. Across five independent 150-round longitudinal trials, Full DART achieves a mean task success rate of 93.33 +/- 2.26%, output quality of 0.9357 +/- 0.0117, retry rate of 0.2307 +/- 0.0816, and allocation delay of 1.1153 +/- 0.0408 s, consistently outperforming its ablated configurations DART isolates persistent and intermittent malicious agents, obtaining a 99.3% output containment rate and restoring system success to 99.8%. These results demonstrate the potential of coupling reputation, incentives, DAG-based coordination, and verifiable blockchain-enabled governance to support adaptive and accountable multi-agent collaboration.

A Black Box for Agentic Processes: Blockchain-Anchored Evidence for AI Agent Communication, Human Oversight, and GRC Audits

Autonomous AI agents increasingly communicate with other agents, invoke tools, exchange intermediate results, and request human approvals. These workflows create a new auditability problem: organizations must reconstruct what happened, when it happened, which agent or human was involved, which control or policy applied, and whether records were modified afterwards. Motivated by the 2026 OpenAI/Hugging Face incident, this position and architecture paper proposes a product- and vendor-neutral black-box architecture for agentic processes. The architecture 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. We define an evidence model that distinguishes temporal anchoring and artifact integrity from event ordering, capture authenticity, authorized anchoring, and causal traceability. The latter properties require additional architectural controls. We then discuss practical use for Governance, Risk, and Compliance (GRC), including compliance testing, risk-based evidence selection, monitoring evidence streams, incident reconstruction, and regulatory reporting readiness under the EU AI Act, NIS2, and the Cyber Resilience Act (CRA). This position and architecture paper does not present an empirical performance or security evaluation. The approach does not prevent agent misbehavior or prove semantic truth. Rather, it strengthens the evidentiary basis for later verification of critical process traces.

XManuChain: A Permissioned Blockchain Framework for Securing IoP Transactions in Production Process Collaboration

A survey of blockchain transaction processing and scalability: toward a trust infrastructure for AI agents

This survey investigates the transaction processing and expanding methods from different perspectives, and presents the existing technologies systematically and highlights their importance.