Dedalus Orchestrator AI Coaching System

ML & AI · flowchart diagram · unknown license

This diagram illustrates the Dedalus Orchestrator, an AI-powered coaching system that processes user queries, delegates analysis to specialized agents, int

Source: https://github.com/akhilm2223/TT-HACKCOLUMBAI/blob/e1b91835e7f27f476ef35bd29fcf502398ffa8dd/README/README.md
Curated by akhilm2223
AI Coaching Orchestrator Multi-agent Snowflake Data Pipeline System Design

Mermaid source

graph TD
    %% Nodes
    User([User / Player])
    Query[Targeted Query]
    Orchestrator{{Dedalus Orchestrator}}

    %% Agents
    Bio[Biomechanics Agent]
    Tac[Tactical Agent]
    Men[Mental Agent]

    %% Snowflake Tools
    MCP[Snowflake MCP Tool]
    History[(Player History DB)]

    %% Output
    Synthesis[Insight Synthesis]
    Report[Coaching Report]

    %% Connections
    User --> Query
    Query --> Orchestrator

    Orchestrator -->|Delegates| Bio
    Orchestrator -->|Delegates| Tac
    Orchestrator -->|Delegates| Men

    Bio -->|Returns Analysis| Orchestrator
    Tac -->|Returns Analysis| Orchestrator
    Men -->|Returns Analysis| Orchestrator

    Orchestrator -.->|Tool Call| MCP
    MCP <-->|SQL & Vector Search| History
    MCP -.->|Returns Context| Orchestrator

    Orchestrator --> Synthesis
    Synthesis --> Report
    Report --> User

What this diagram shows

The diagram depicts a multi-agent AI coaching system. It starts with a user submitting a targeted query, which is processed by the Dedalus Orchestrator. The Orchestrator delegates analysis to specialized Biomechanics, Tactical, and Mental Agents. It also interacts with the Snowflake MCP Tool to query the Player History DB for historical context. Finally, it synthesizes insights from all sources to generate a coaching report for the user.

When to use it

This pattern is useful for designing intelligent systems that require complex analysis, delegation of tasks to specialized modules, and integration with historical data. Ideal for AI-driven coaching, personalized recommendation engines, or diagnostic tools in various domains.

How to adapt it for your project

This system can be adapted by adding more specialized agents for different analysis types (e.g., nutrition, recovery), integrating with other data sources beyond player history (e.g., real-time sensor data), or implementing different synthesis algorithms. The Snowflake MCP tool could be replaced with other data warehousing or vector search solutions.

Key concepts