Building trade assistant: How Jefferies optimized front office trading operations with AI
AWS Machine Learning Sanjay Nagraj
Jefferies built an agentic AI trade assistant using Claude, Amazon Bedrock, and Model Context Protocol tools to let traders query millions of rows of equities data in natural language without coding or IT delays. The system processes trader queries through an eight-step workflow with retrieval-augmented generation for schema context, row-level security controls, and dynamic SQL generation, enabling real-time sector breakdowns and trend visualizations. Traders now spend less time on data wrangling and dashboard requests, freeing IT teams for strategic work while democratizing data-driven decision-making across trading desks.
Why it matters
In this post, we explore how Jefferies overcame these challenges with a solution built on Strands Agents, an agent harness SDK for building AI agents that can reason, plan, and act by orchestrating calls to foundation models (FMs) and external tools. The solution uses large language models (LLMs), Amazon Bedrock, and Amazon Bedrock Knowledge Bases. It also uses Model Context Protocol (MCP), an open standard that helps AI agents securely connect to diverse data sources and tools through a unified interface. We cover the solution overview, the rationale for selecting the underlying technology stack, lessons learned, and the business impact the solution created at Jefferies.
