The article examines how AI agents facilitated the migration of a data lake from Snowflake to BigQuery. It highlights the challenges involved in transferring large datasets, including data integrity and compatibility issues. By employing AI-driven automation, the process became more efficient and less error-prone. The AI agents managed data mapping and transformation tasks, significantly reducing the manual workload for engineers. A key focus is on the role of machine learning models in optimizing data handling. These models helped in identifying patterns and anomalies during the migration, allowing for quicker troubleshooting. The integration of AI not only sped up the migration timeline but also improved the overall quality of the data in BigQuery. Metrics are provided, showing a reduction in migration time by up to 40% compared to traditional methods. The article also discusses the importance of collaboration between data engineers and AI tools. It emphasizes that successful migration requires a deep understanding of both the source and destination platforms. As organizations increasingly rely on cloud data solutions, the insights gained from this migration process can inform future projects, making it a relevant case study for data management professionals.