Prevent Hallucinated Responses from any AI Agent

AI agents are increasingly being used in customer support, but they often produce incorrect or misleading responses, which can erode trust with customers. Even with tools and external data sources, AI agents can still hallucinate or return flawed information, particularly in high-stakes situations like customer support. To address this issue, Cleanlab provides a real-time trustworthiness scoring system that analyzes user prompts, AI responses, tool outputs, and internal LLM calls to generate a trust score between 0 and 1. This scoring system can flag incorrect responses, allowing for fallback strategies such as routing conversations to human agents or providing safe, generic responses. By integrating Cleanlab with frameworks like LangGraph, businesses can ensure that their AI agents provide accurate and reliable responses, preventing flawed information from reaching customers and protecting their trust. Cleanlab's detection capability is benchmarked as the most accurate real-time method for detecting bad responses from any LLM, and it works without requiring labeled data or model training infrastructure.

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