Jefferies用Strands Agents和Amazon Bedrock造了个交易助手，能自动调用工具处理数据，相比传统方案更灵活。
Jefferies基于Strands Agents agent harness SDK，结合Amazon Bedrock和Bedrock知识库构建了前台交易助手。该方案利用大语言模型与Model Context Protocol（MCP）安全连接数据源和工具。AI代理通过编排基础模型调用和外部工具实现推理、规划和执行。该交易助手优化了Jefferies的前台交易操作流程，提升了运营效率。
Building trade assistant: How Jefferies optimized front office trading operations with AI
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.