arXiv:2606. 26028v2 Announce Type: replace-cross Abstract: As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy?
By Xihan Xiong, Zelin Li, Wei Wei, Qin Wang, William Knottenbelt, Zhipeng Wang
arXiv:2605. 06738v2 Announce Type: replace-cross Abstract: Autonomous AI agents already transact at production scale -- 69,000 bots, 165 million transactions, $50 million in volume on a single marketplace -- and any party can verify a signed credential without a central service.
By Lars Kersten Kroehl
arXiv:2609.22944v1 Announce Type: cross
Abstract: Autonomous AI agents increasingly act across organizational boundaries on behalf of human operators: they invoke third-party services, delegate subta...
By Oliver Aleksander Larsen, Mahyar Tourchi Moghaddam
arXiv:2609.21325v1 Announce Type: new
Abstract: Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of su...
By Steve Drew, Jiayu Zhou
The paper introduces the Agentic Commerce Bench (ACB), a benchmark for measuring fraud in AI agents that autonomously spend money. It presents a taxonomy of agentic commerce fraud, a dataset of twenty fraud classes derived from real production data, and an open‑source detector stack called gordonguard for auditing and replaying hostile counterparties. The study shows that current reasoning layers and security scanners perform poorly on many classes, highlighting the need for better detection mechanisms.
By Ankit Srivastava, Debjyoti Paul
The paper introduces DART, a Directed Acyclic Graph (DAG)-based framework that combines centralized orchestration with blockchain-enabled decentralized governance to manage reputation and incentives in large language model (LLM)-based multi-agent systems. DART dynamically allocates tasks based on agent capability, reputation, and workload, while continuously updating trust scores through post-execution evidence and smart contract accountability. Experimental results show that DART outperforms centralized baselines, achieving high task success rates, low retry rates, and effective containment of malicious agents.
By Manoj Kumala, Xinyun Liua, Ronghua Xu