Beyond Prompting: Building Scalable AI with Multi-Agent Systems and MCP

Stop building monolithic LLM agents. Learn how a microservices approach using multi-agent systems creates truly scalable and powerful AI.

#1about 4 minutes

Understanding LLMs, context windows, and RAG

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.

#2about 3 minutes

Transitioning from monolithic agents to multi-agent systems

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.

#3about 3 minutes

Why AI projects fail and the need for evaluation

Most AI proofs-of-concept fail in production due to a lack of business context, highlighting the need for robust evaluation frameworks like MLflow.

#4about 4 minutes

Demo: Evaluating an agent's RAG tool using 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.

#5about 3 minutes

Introducing the Model Context Protocol for tool integration

The Model Context Protocol (MCP) standardizes how agents connect to tools, creating a reusable ecosystem of servers and clients to avoid redundant integration work.

#6about 6 minutes

Demo: Automating workflows with MCP servers and clients

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.

#7about 2 minutes

Exploring the current limitations and future of MCP

The current challenges for MCP include tool discovery, agent confusion from overlapping server functions, and the lack of a centralized security gateway.