Oatmeal-Farm-Network/ag-agent

AI Summary: This task requires researching short-term and long-term memory mechanisms for conversational AI, implementing a chat history feature within the existing `ag-agent` agricultural advisor application, and investigating how Memory-Centric Programming (MCP) can improve the application's workflow. The implementation will involve modifying the existing Python codebase, potentially integrating new libraries, and testing the functionality within the Streamlit UI.

AI Summary: This task involves containerizing the existing Streamlit web application, creating a GitHub repository for the web development team, and deploying the application to Azure using a serverless approach with CI/CD integration. This includes setting up Azure resources and configuring the CI/CD pipeline to automate the deployment process.