In a rapidly evolving AI landscape, autonomous AI agents are being deployed to interact with real-time analytics databases, marking an interesting shift in how we think about data systems. The emergence of these agents as active users of real-time analytics databases marks both an opportunity and a challenge for the industry. As these agents become more autonomous and their deployment more widespread, new patterns in how they interact with data systems will likely emerge, leading to new optimizations and features. Real-time analytics databases are already being optimized to support high-throughput and exploratory workloads, with features such as improved discoverability, LLM-friendly documentation, scaling for AI workloads, server-side state for AI memory, customized access control models, and a standardized way to connect LLMs with the context they need during specific tasks. The ClickHouse MCP Server is an example of this evolution, providing a bridge between AI-powered applications and data sources, enabling LLMs to list databases on the ClickHouse instance connected, list tables, and run select queries. As organizations continue to deploy AI agents at scale and new use cases emerge, the relationship between agents and real-time databases will likely continue to evolve in ways yet to be anticipated.