Multi-Agent MCP Skillset Architecture

A reference architecture for an AI system composed of multiple agents, each with its own skillset, coordinated through a main MCP server integration.

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This blueprint demonstrates a multi-agent architecture where several AI agents, each with its own specialized skillset, are coordinated through a central MCP server integration.

Each agent is designed to handle specific tasks or domains, allowing for specialization and improved performance in their respective areas. The main MCP server integration serves as the orchestrator, managing communication and coordination among the agents.

This architecture exemplifies the effective use of ChatBotKit's resource management and skillset capabilities to create a modular AI system that can tackle complex problems through collaboration. The design allows for easy scalability and adaptability, making it suitable for various applications where multi-agent cooperation is essential.

This architecture is particularly useful in scenarios where different very specialized agents need to be made available for more general purpose chat assistants like Anthropic Claude or ChatGPT. It is also useful to build dedicated tools for developers in order to perform fast and consistent troubleshooting without the need to provision per-developer access credentials which may be a security risk.

Skillset

This example uses a dedicated Skillset. Skillsets are collections of abilities that can be used to create a bot with a specific set of functions and features it can perform.

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    Call Agent 1

    Description under what conditions to call agent 1
  • 🇩🇪

    Call Agent 2

    Description under what conditions to call agent 2
  • 🇩🇪

    Call Agent 3

    Description under what conditions to call agent 3

Terraform Code

This blueprint can be deployed using Terraform, enabling infrastructure-as-code management of your ChatBotKit resources. Use the code below to recreate this example in your own environment.

View Terraform Code

Copy this Terraform configuration to deploy the blueprint resources:

Next steps:

  1. Save the code above to a file named main.tf
  2. Set your API key: export CHATBOTKIT_API_KEY=your-api-key
  3. Run terraform init to initialize
  4. Run terraform plan to preview changes
  5. Run terraform apply to deploy

Learn more about the Terraform provider

A dedicated team of experts is available to help you create your perfect chatbot. Reach out via or chat for more information.

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