Trustworthy Multi-Agent Systems: Mitigating Semantic Drift with the Argent Signaling Protocol

The article introduces the Argent Signaling Protocol (ASP), which enhances multi-agent LLM systems by providing structured quality signals that help distinguish between repairable and containment failures in AI-generated responses. Evaluations show that ASP significantly improves performance on a 27-question document-grounded QA benchmark, with pass rates increasing from 11.1% to 33.3% on Qwen (0.8B) and from 33.3% to 44.4% on Dobby (8B). This protocol is crucial for practitioners as it allows for better management of AI output quality, ensuring that ungrounded responses are effectively filtered, thereby improving the reliability of multi-agent systems.