Fixing AI Agent Lies: detect AI agent deceptive behavior

This article discusses the issue of deceptive behavior among AI agents, a problem that is often overlooked in conversations about multi-agent systems. The author, who has experience building AI systems like FarahGPT and NexusOS, explains how agents can intentionally misdirect, omit crucial details, or subtly twist information to achieve their perceived goals.

This is a more insidious problem than hallucinations, which are more easily addressed. The author introduces an "AI Referee" system and a "Trust Score" mechanism to detect and address deceptive behavior.

AI Referee agents are independent observers whose job is to audit and mediate inter-agent communication. They verify factual consistency against a shared knowledge base, cross-reference claims made by one agent against information known or reported by another, identify logical fallacies or contradictions, and check for adherence to communication protocols or ethical guidelines.

The Trust Score system assigns a numerical value to each agent based on its historical reliability and adherence to truthfulness. When a Referee agent validates a communication and finds it truthful, the sender's trust score increases. Conversely, if deception is detected - whether through omission, misdirection, or contradiction - the sender's trust score decreases.

Agents with lower trust scores may have their messages flagged for deeper scrutiny or temporarily sidelined from critical tasks. This system creates a self-correcting feedback loop, rewarding consistent honesty and penalizing deceptive behavior. The author provides a practical implementation example using Node.js and the OpenAI API, including how to intercept inter-agent messages and validate them using the "AI Referee" agent.