Large language models use tokens to process text, but their limited context window requires retrieval-augmented generation (RAG) to access large or real-time datasets.
Giving a single LLM agent too many tools leads to confusion and failure, so a multi-agent architecture with specialized agents improves performance and scalability.
Most AI proofs-of-concept fail in production due to a lack of business context, highlighting the need for robust evaluation frameworks like MLflow.
A practical demonstration shows how to use MLflow with custom metrics like relevance and specificity to identify and fix agent hallucinations in a RAG tool.
The Model Context Protocol (MCP) standardizes how agents connect to tools, creating a reusable ecosystem of servers and clients to avoid redundant integration work.
This demo showcases connecting local files, GitHub, and Databricks Genie as MCP servers to a client, enabling complex, automated workflows from a single chat interface.
The current challenges for MCP include tool discovery, agent confusion from overlapping server functions, and the lack of a centralized security gateway.
24:12Brad Axen
24:22Philipp Schmid
26:35Ricardo
29:03Jörg Neumann
23:17Angie Jones
39:11Thomas Dohmke
23:33Oliver Will
23:39Dennis Zielke & Rene Pajta
Jobs that call for the skills explored in this talk.
Deutsches Medizinrechenzentrum GmbH