AI agents are increasingly used to automate data-driven workflows, but efficiently managing large datasets remains a challenge due to the limitations of popular libraries like Pandas. Bodo DataFrames addresses this issue by providing distributed execution and HPC-grade performance to standard Pandas code without requiring significant refactoring. By integrating Bodo with LangChain, AI agents can handle large-scale datasets, such as the NYC Taxi's billion-trip dataset, with much greater efficiency. For instance, using Bodo, an AI agent can swiftly analyze and answer complex queries about real-world data, such as estimating taxi fares between specific locations, which would otherwise be impractical with standard Pandas on smaller systems. This approach allows AI agents to scale their operations, leveraging Bodo's compatibility with Pandas and its MPI-based parallel backend to process large datasets without running into memory errors. The example demonstrates Bodo's effectiveness by completing a complex data query in just 4.5 minutes on a 2024 MacBook Pro.