AI data engineers will work alongside human data teams to carry out tasks such as building data assets, investigating ongoing issues, and optimizing costs. For an AI agent to be a helpful teammate, it needs a comprehensive understanding of the existing underlying data assets, from raw data through transformations and semantic modeling to reporting, and the ability to make changes to these assets as it reasons and goes through the chain-of-thought process. The value added to the business is increased productivity, with AI leveling up every data professional and enabling less technical team members to contribute to areas they couldn't before. The architecture for AI agents will require advancing fundamental enabling technologies such as LLMs and infrastructure for AI agents to retrieve, understand, and modify data assets. Future AI data engineers will rely on reasoning and chain-of-thought processes to perform actions for given tasks, and the field is moving towards code-first workflows as the primary way to manage data assets, with AI agents consuming code as input and producing it as output.