AI PMs looking to build scalable AI applications should consider MCP server integrations as a strategic enabler for managing and processing large volumes of data. The recent course from Deeplearning. ai, which focuses on building AI apps via MCP servers to extract structured data from PDF invoices stored in Box folders, offers several actionable insights.
First, understand the dual approach presented: comparing local, do-it-yourself processing with an MCP server approach that offloads tasks such as file search, text extraction, and LM integration. By leveraging MCP servers, the complexity of building and maintaining custom code for file management is significantly reduced.
In planning your product roadmap, assess the ease of integration with your existing data infrastructure. Consider experimenting with a pilot project that utilizes an MCP server to manage similar file processing tasks, evaluating parameters like processing speed, error rates, cost efficiency, and scalability.
Next, as the application evolves into a multi-agent system using ADA protocols, systematically design components for independent agents that communicate effectively. This modular approach allows for incremental improvements and easier troubleshooting.
In summary, this integration not only streamlines operational overhead but also provides an agile framework for scaling AI functionalities across diverse data environments.