FoundationaLLM can power anomaly detection scenarios against both structured and unstructured data. In this example, the user describes in plain English the description of a product and the LLM is used to evaluate it against a SQL database containing product data. The agent learns from the product database what a normal product of this type “looks” like by generating and issuing several queries and calculating statistics over the data. The resultant statistics are provided to the agent to compare against so it can reason, identify the parts that make the product an anomaly, and describe why that is an anomaly to the user.