AGREE Framework

What is the AGREE Framework?

When companies deploy AI systems for document-based tasks, the question “Does the answer sound right?” is not enough. AGREE provides a structured methodology to check each individual statement of an AI response against the sources — from fully supported to direct contradiction. Combined with guardrails, this creates a robust quality assurance system for AI-generated content.

The AGREE Framework (Assessing Grounding of Generated Responses from Evidence) is an evaluation system developed by Google that systematically assesses the source-groundedness (grounding) of LLM responses. It goes beyond simple fact-checking and analyzes whether each individual statement in an AI response is supported by the provided source documents — or whether the model inserts unsupported claims.

The framework breaks down LLM responses into individual statements (claims) and checks each against the source documents. Different grounding types are distinguished: fully supported statements, partially supported statements, unsupported but plausible statements, and direct contradictions to the sources. This granular analysis precisely shows where a model works reliably and where it tends toward confabulation. Automated and human evaluation are combined.

For companies deploying AI systems for document-based tasks, AGREE provides a structured methodology for quality assurance. Instead of just checking whether an answer “sounds right,” you can systematically measure how source-grounded each statement is. In combination with the FACTS Grounding Benchmark and Guardrails, this creates a robust quality assurance system for AI-generated content.