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Build a Multi-Agent App with MCP & MongoDB: Promotion Tycoon

November 2025
Webinar Preview

Session Resources + Slides (Coming Soon!)

Summary

The session "Build a Multi-Agent App with MCP & MongoDB: Promotion Tycoon" is designed for developers interested in AI agent systems and MongoDB. It explores the creation of a multi-agent application using Model Context Protocols (MCP) and MongoDB, focusing on a practical case study called "Promotion Tycoon." This app aims to assist users in career advancement by automating the process of gathering and analyzing professional achievements. The session covers the architecture of a multi-agent system, utilizing MongoDB for data storage and LangGraph for agent orchestration. It highlights the use of Gradio for the user interface and Tableau for data search. The discussion includes practical challenges, such as handling API keys and troubleshooting, while emphasizing the importance of persistence and human-in-the-loop features. The session also touches on the design choices for agent systems, the role of MCP, and the integration of various tools to enhance functionality.

Key Takeaways:

  • Understanding the architecture of a multi-agent system using MCP and MongoDB.
  • The importance of persistence and human-in-the-loop features in agent systems.
  • Utilizing Gradio for UI and Tableau for data search in building applications.
  • Design choices and challenges in developing multi-agent workflows.
  • Strategies for integrating multiple tools and managing agent communication.

In-Depth Discussions

Multi-Agent System Architecture

The session explores the archite ...
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cture of a multi-agent system, focusing on the use of Model Context Protocols (MCP) and MongoDB. The application, "Promotion Tycoon," is designed to assist users in career advancement by automating the collection and analysis of professional achievements. MongoDB is used for data storage and persistence, while LangGraph handles agent orchestration. The system comprises several agents, each with specific roles, such as project curation and impact analysis. The architecture emphasizes flexibility and control, allowing for a mix of autonomous and guided workflows. The session highlights the importance of defining clear agent roles and communication pathways to ensure efficient operation.

Persistence and Human-in-the-Loop Features

Persistence is a key feature in multi-agent systems, acting as short-term memory to maintain context throughout interactions. MongoDB's built-in persistence capabilities are leveraged to store checkpoints and conversation history, ensuring the system remains aware of user inputs and actions. The session also discusses the human-in-the-loop feature, which allows for manual intervention at various stages of the workflow. This feature is particularly useful for testing and refining agent behavior, enabling developers to control token costs and improve prompt engineering. The balance between automation and human oversight is essential for creating effective and user-friendly applications.

User Interface and Data Search Integration

Gradio is used to build the user interface for the "Promotion Tycoon" application, providing a simple and interactive platform for users to input data and receive feedback. Tableau is integrated for data search, enabling the application to gather relevant information about target roles and professional competencies. The session explores the design choices behind these integrations, emphasizing the need for smooth interaction between the UI and backend processes. By combining Gradio and Tableau, the application offers a comprehensive solution for users seeking to enhance their career prospects through data-driven insights.

Design Choices and Challenges

Developing a multi-agent system involves several design choices and challenges, as highlighted in the session. Key considerations include the selection of tools and technologies, the definition of agent roles, and the management of agent communication. The session addresses common challenges such as handling API keys, troubleshooting errors, and optimizing performance. It also discusses the trade-offs between flexibility and control, particularly in the context of using MCP and LangGraph. By sharing practical insights and experiences, the session provides valuable guidance for developers looking to build effective and scalable multi-agent applications.

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