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We talk a lot about LLMs, RAG, vector databases, and multi-agent systems. But there's another important question: How do AI agents access and understand enterprise knowledge? Google's Open Knowledge Format (OKF) aims to make knowledge more structured, portable, and accessible to both humans and AI agents. Instead of scattering business definitions, database schemas, and relationships across different documents and tools, OKF organizes knowledge using familiar formats like Markdown and YAML. How is it different from RAG? RAG retrieves relevant information to answer a query. OKF provides a standardized way to organize and represent knowledge. They aren't competitors. You can combine OKF for structured business knowledge with RAG for searching large document collections. However, OKF doesn't replace databases, search engines, or access-control systems. Knowledge still needs to be maintained, verified, and retrieved appropriately. My takeaway: Building better AI agents isn't just about choosing a better model. It's also about how we structure, govern, and make knowledge accessible to those agents. Could open knowledge formats become a standard component of enterprise AI architectures? Official specification: https://github.com/GoogleCloudPlatform/open-knowledge-format

Hey everyone. I am a financial professional with over 5 years of work experience. I am interested to learn about practical use cases of AI in my field. I have completed the generic genAI trainings. Can someone guide me on how can I begin?