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Cloth knowledge brokers work with current OneLake implementations, giving them a base set of information to make use of as context to your queries. Alongside along with your knowledge, they are often fine-tuned utilizing examples or be given particular directions to assist construct queries.
There are some stipulations earlier than you possibly can construct an information agent. The important thing requirement is an F64 or larger consumer, together with an appropriate knowledge supply. This could be a lake home, an information warehouse, a set of Energy BI semantic fashions, or a KQL database. Limiting the sources is sensible, because it reduces the danger of shedding the context related to a question and retains the AI grounded. This helps make sure the agent makes use of a restricted set of identified question varieties, permitting it to show your questions into the suitable question.
Constructing AI-powered queries
The agent makes use of person credentials when making queries, so it solely works with knowledge the person can view. Position-based entry controls are the default, holding your knowledge as safe as attainable. Brokers’ operations have to keep away from leaking confidential info, particularly in the event that they’re to be embedded inside extra advanced Azure AI Foundry agentic workflows and processes.
