Building trade assistant: How Jefferies optimized front office trading operations with AI

<p>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.</p> <p><a href="https://www.jefferies.com/" target="_blank" rel="noopener">Jefferies</a>, 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.</p> <p>In this post, we explore how Jefferies overcame these challenges with a solution built on <a href="https://strandsagents.com/" target="_blank" rel="noopener">Strands Agents</a>, 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 <a href="https://aws.amazon.com/what-is/large-language-model/" target="_blank" rel="noopener">large language models (LLMs)</a>, <a href="https://aws.amazon.com/bedrock" target="_blank" rel="noopener">Amazon Bedrock</a>, and <a href="https://aws.amazon.com/bedrock/knowledge-bases/" target="_blank" rel="noopener">Amazon Bedrock Knowledge Bases</a>. It also uses <a href="https://aws.amazon.com/blogs/machine-learning/unlocking-the-power-of-model-context-protocol-mcp-on-aws/" target="_blank" rel="noopener">Model Context Protocol (MCP)</a>, 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.</p> <h2 id="solution-overview">Solution overview</h2> <p><img src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/07/21/ML-20564-1.png" alt="Architecture of the Front Office trade assistant showing the domain-specific agent, MCP tools, and connected data sources" width="800"></p> <p>The Front Office trade assistant represents a shift in how Capital Markets’ Front Office Equity traders interact with data. At the core of the solution is a domain-specific agent that drives conversation with traders through natural language text. A set of MCP tools lets the agent connect with data sources, including trade data repositories, Financial Information Exchange (FIX) message files, and in-memory databases. When a trader submits a query to the trade assistant, Amazon Bedrock invokes an LLM (Anthropic Claude) to interpret the natural language intent, generate the corresponding SQL, and run it against underlying data sources to surface the response. The solution has a conversational interface, which allows traders to drill down on topics and explore data insights conversationally. The solution maintains conversation context to provide relevant insights and suggestions for deeper analysis over the entire lifetime of the session.</p> <h2 id="how-the-solution-works">How the solution works</h2> <p>The solution integrates with Jefferies’ existing trading infrastructure through an eight-step process depicted in the following architecture diagram. From a security perspective, the solution uses <a href="https://aws.a