Demonstration of building an agentic AI application to support financial analysts with a conversational AI assistant, including architectural components (Anthropic Claude 3.5 Sonnet, Amazon Bedrock, Elasticsearch Vector Database, Elasticsearch MCP Server) and capabilities such as pattern identification, linking news sentiment to portfolio performance, and real-time natural language data engagement.

Scaling Agentic AI with Claude, MCP, and Vectors. We'll focus on a financial services Agentic AI case study that empowers analysts with a conversational AI assistant built using Anthropic Claude 3.5 Sonnet on Amazon Bedrock. Elasticsearch vector database. Elasticsearch MCP (Model Context Protocol) Server. This assistant transforms complex workflows—like assessing the impact of market news on thousands of customer portfolios—into an intuitive, natural language dialogue. We'll demonstrate how to build and deploy AI Agents that help: Rapidly identify patterns in complex financial data; Build meaningful correlations, such as linking news sentiment to portfolio performance; Engage with your data in real-time, using natural language. We'll also highlight how MCP servers can integrate additional services, such as weather data and email notifications, demonstrating the power of search and generative AI.