This article provides a step-by-step guide to creating an AI data agent that allows business users to query data using natural language, bypassing the need for SQL. The author demonstrates this by building the "Avocado Sales Analytics Agent" using Google Cloud Platform's BigQuery Conversational Analytics API.

The process involves uploading a dataset (Kaggle's Avocado Prices), understanding its schema, and then configuring the agent. Crucially, clear and precise instructions are essential for the AI to accurately query data, avoid errors, and provide relevant answers. These instructions define table and column definitions, metric calculation rules, and date handling logic.

The guide emphasizes principles for writing effective instructions, such as clarity, providing examples, setting boundaries, and defining roles. It also highlights common mistakes to avoid, like incorrect price calculations or confusing volume units, and notes data quality considerations, particularly regarding geographical data aggregation.