AI Agent Quality Assurance

AI Agent Quality Assurance (AI Agent QA) is a specialized field of quality management focused on the performance and reliability of conversational AI systems, including Large Language Model (LLM)-based agents. Unlike traditional human QA, AI Agent QA leverages automated observability and evaluation to monitor 100% of AI-customer interactions.

**Definition**: AI Agent Quality Assurance is the practice of ensuring AI-powered conversational agents deliver accurate, safe, and brand-compliant responses through continuous monitoring, automated evaluation, and real-time intervention.

**Key Distinction from Traditional QA**: Traditional QA samples 2-5% of human agent interactions. AI Agent QA analyzes 100% of AI interactions in real-time, detecting AI-specific issues like hallucinations, grounding failures, and brand safety violations that traditional QA cannot identify.

1. Hallucination Detection: Identifying instances where the AI agent provides factual information that is not grounded in the provided knowledge base or business ontology.

2. Grounding & Factual Accuracy: Ensuring that every claim made by an AI agent is supported by verified data sources, documents, or system integrations.

3. Brand Safety & Voice: Monitoring AI responses to ensure they maintain the correct tone, avoid bias, and stay within the predefined brand guidelines and safety guardrails.

4. Conversational Flow: Evaluating the coherence, helpfulness, and resolution efficiency of the AI agent in solving customer problems.

5. Compliance Monitoring: Ensuring AI agents adhere to industry regulations (e.g., HIPAA, GDPR) and internal business rules during every interaction.

Oversai's approach to AI Agent QA uses 'AI to evaluate AI', employing advanced models to score interactions based on custom rubrics, ensuring that organizations can scale their AI operations with 100% confidence.