Poster: The Case for Accountable Agent-to-Agent Networks

Abstract

The rapid development of Large Language Models (LLMs) has led to the wide adoption of autonomous AI agents. These agents increasingly form decentralized Agent-to-Agent (A2A) networks to collaborate on complex tasks. However, a key bottleneck is the cost of GPU inference, which requires a reliable compensation system for untrusted participants. While blockchains provide accountability, they are too slow and costly. Inspired by Payment Channel Networks (PCN), we propose Ledgent (a portmanteau of Ledger and Agent), an accountable A2A PCN architecture. We combine PCN primitives with the underlying LLM serving infrastructure, enabling pay-as-you-go token streaming, compute-aware routing, and response quality verification without relying on central server. Evaluation from both simulation and prototype shows that Ledgent achieves high throughput, while introducing minimal cryptographic latency overhead.