If you manage a front office trading desk at investment banks, you know the challenge: traders need real-time insights into client behavior, trade patterns, and market trends from vast amounts of data to make split-second decisions. However, they rarely have the time during the day, nor the coding ability, to build and maintain systems capable of delivering those insights. With millions of rows of data spread across multiple visualization tools, achieving end-to-end visibility is difficult. Traditional approaches force traders to rely on subject matter experts for analysis and collaborate with IT teams to build custom dashboards. The process can take days or weeks. The result is a widening gap between the data available and the decisions it should be informing.

Jefferies, a global full-service investment banking firm, recognized this challenge as an opportunity to apply agentic AI to optimize how its equities trading desks operate. By building an agentic AI trade assistant on AWS, Jefferies set out to put the power of real-time data analysis directly in traders’ hands without the requirement of coding, waiting in IT queues, and with no compromise on accuracy.

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.