Field technicians managing heavy farm machinery face a critical operational challenge: diagnosing complex failures without immediate access to specific parts often results in unnecessary site visits, extended equipment downtime, and significant financial loss. To mitigate these risks during peak harvest seasons, organizations are deploying AI-powered assistants that enable remote diagnosis via natural language interaction. This solution leverages **Amazon Nova 2 Lite** as the foundation model within an AgentCore Runtime environment, utilizing retrieval-augmented generation (RAG) to index vast repositories of equipment manuals and parts catalogs.
Authentication Layer with AWS Cognito
The architecture begins at Section A, which handles user authentication before any diagnostic logic executes. Amazon Cognito User Pools manage identity verification for field technicians accessing the system from mobile devices or ruggedized terminals in remote locations.
This layer ensures that only authorized personnel can query sensitive repair procedures and parts inventory data. The frontend application, hosted on AWS Amplify, acts as a secure gateway to AgentCore-hosted agents running within VPCs configured for private connectivity via Direct Connect or PrivateLink. This separation of concerns allows the agent runtime to remain isolated from public internet exposure while still serving authenticated requests.
Agent Core Runtime and Strands SDK Integration
The core intelligence resides in Section B, where AgentCore Runtime orchestrates tasks using the Strands Agents SDK. This framework enables developers to define agent behaviors that query a Bedrock Knowledge Base containing indexed documentation.
In practice, an engineer might configure the Strands graph to first attempt retrieving specific error codes from manufacturer manuals before invoking diagnostic logic. The system maintains conversation history using AgentCore Memory, allowing technicians to ask follow-up questions like "What if I don't have that part?" without needing to restate context.
This persistence is crucial for complex troubleshooting scenarios where a single issue requires multiple steps of reasoning across different documentation sources. Developers should ensure the memory store scales appropriately as conversation volume increases during critical maintenance windows, potentially utilizing DynamoDB or managed vector stores depending on latency requirements.