Natural Language to SQL Agent for CRUD Operations

In this project, we developed an Agentic AI system capable of performing full CRUD (Create, Read, Update, Delete) operations on an SQL database using natural English instructions. Powered by a Large Language Model (LLM), the agent translates user queries into SQL commands, enabling seamless database management without the need for technical SQL knowledge. The core functionality allows users to interact with the database using simple English commands such as “Add a new employee named John,” “Show all products with price over $100,” or “Delete all inactive users.” The system intelligently parses these instructions, understands the intent, and generates accurate and safe SQL queries accordingly. This solution drastically reduces the barrier for non-technical users to manage data and improves productivity by eliminating the need to write or understand SQL syntax. It also includes safety checks and confirmations before performing critical actions like deletions or updates, ensuring data integrity and security. During testing, the agent successfully handled a wide range of use cases across multiple tables and relational scenarios. It demonstrated high accuracy in query generation and was able to handle both simple and moderately complex operations, including conditional filtering, table joins, and field-specific updates. The outcome of this project highlights the powerful potential of combining LLMs with databases to create human-centric data interaction systems. It lays the groundwork for building intelligent data assistants that bridge the gap between users and structured data environments.