Grounding ensures that AI agent outputs are based on trusted, verified sources — enterprise documents, databases, real-time data — rather than on what the model generates from its training data alone.
A grounded agent that retrieves the current version of a policy before answering a question about it is far less likely to produce a plausible-sounding but incorrect answer than an ungrounded one. Grounding does not eliminate the risk of hallucination entirely, but it dramatically reduces it by anchoring the model's responses to actual source material that can be cited and verified.
