Managing equipment repairs for heavy farm machinery often requires technicians to diagnose issues without the right parts, leading to multiple site visits, extended downtime, and substantial financial losses, especially during harvest season.

In this post, you build an AI-powered equipment repair assistant using Amazon Bedrock AgentCore that helps farmers and field technicians diagnose equipment problems, identify required parts, and access manufacturer-approved repair procedures through natural language. The solution uses AgentCore Runtime with the Strands Agents SDK, Amazon Nova 2 Lite as the foundation model, Amazon Bedrock Knowledge Base for retrieval-augmented generation (RAG), and AgentCore Memory for conversation persistence.

Solution overview

This solution combines a web frontend with an AgentCore-hosted agent that answers equipment diagnostic questions using indexed manufacturer documentation.

Amazon Cognito manages user authentication, and AWS Amplify hosts the web application. The equipment repair agent runs on AgentCore Runtime, built with the Strands Agents SDK. It queries a Bedrock Knowledge Base containing indexed equipment manuals, parts catalogs, and repair documentation. AgentCore Memory maintains conversation history across sessions so technicians can ask follow-up questions without repeating context.