Pantheon ($EON)
  • Welcome
  • Welcome to Pantheon (EON)
    • Introduction to Pantheon (EON)
      • What is Pantheon (EON)
      • Vision & Philosophy
    • Why Pantheon?
      • Challenges Addressed to EON
      • Use Cases & Applications
    • Technology Foundations
      • Overview of Key Technologies
      • Comparisons with Traditional AI Architectures
  • The Pantheon (EON) Ecosystem
    • User Journey
      • User Workflow: From Prompt to Project
  • The Pantheon (EON) Core
    • Overview
      • Core Principles
      • End-to-End AI Workflow
    • Distributed AI Registry
    • Orchestrators
      • Task Management and Resource Allocation
      • Project Mining
    • Agents
      • Execution Lifecycle
      • Integration with Tools & Memory Systems
    • Tools
      • Atomic Functionality and Monetization
      • Development and Registration Guidelines
    • Projects
      • Building Projects
      • Security & Configuration
  • The Knowledge Layers
    • Overview
    • Shared Memory
    • Private Memory
  • Data Sources
    • Real-Time Data Ingestion
    • Data Schemas
    • Event Listeners
  • Security Control
    • Access Control
    • Registry Security
    • Data Security
    • Tool Security
  • Development & Contribution
    • Frequently Asked Questions
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  • End-to-End Workflow Steps
  • 1. User Request Submission
  • 2. Orchestrator Queries the Registry
  • 3. Workflow Construction
  • 4. Initialization of Agents and Tools
  • 5. Agent Execution with Tools
  • 6. Retrieval-Augmented Generation (RAG)
  • 7. Orchestrator Collects Results
  • 8. User Receives Output
  • Why This Workflow Matters
  • Explore Further
  1. The Pantheon (EON) Core
  2. Overview

End-to-End AI Workflow

PreviousCore PrinciplesNextDistributed AI Registry

Last updated 3 months ago

The End-to-End AI Workflow in the Pantheon (EON) ecosystem demonstrates how a user request is processed and executed through its modular components, including the Orchestrator, Agents, Tools, and Memory Systems. This workflow is designed to ensure efficiency, scalability, and adaptability, enabling seamless delivery of AI-powered solutions.

Below is a step-by-step breakdown of the process, based on the accompanying sequence diagram.

End-to-End Workflow Steps

1. User Request Submission

  • User Input: The process begins when a user submits a high-level request, such as "Launch a Meme Campaign".

  • Role of Orchestrator: The Orchestrator serves as the entry point, interpreting the user’s goal.

2. Orchestrator Queries the Registry

  • Search for Components: The Orchestrator queries the AI Registry to identify relevant Tools, Agents, and workflows.

  • Registry Response: The AI Registry, powered by DHT and IPFS, returns references, metadata, and associated ingestion topics.

3. Workflow Construction

  • DAG Creation: The Orchestrator constructs a Directed Acyclic Graph (DAG) using Ray Workflows, organizing the tasks into a logical sequence.

  • Task Planning: This step includes chaining tools and agents required for the workflow.

4. Initialization of Agents and Tools

  • Agents: The Orchestrator initializes Agents (Ray Actors) to execute specific parts of the workflow.

  • Tools: Atomic functionalities, such as APIs or computations, are also initialized as part of the process.

5. Agent Execution with Tools

  • Tool Invocation: Agents invoke tools to perform atomic tasks. For example, an Agent might call a tool to analyze tweets.

  • Results: The tool returns results to the Agent, enabling further processing.

6. Retrieval-Augmented Generation (RAG)

  • Shared Memory: The Agent queries Shared Memory (Qdrant) for global, reusable knowledge.

  • Private Memory: Project-specific or sensitive data is fetched from Private Memory (LightRAG).

  • Output Generation: The Agent combines retrieved knowledge to refine the task and generate outputs.

7. Orchestrator Collects Results

  • Result Aggregation: Agents return their outputs to the Orchestrator, which consolidates partial or final results.

8. User Receives Output

  • Deployment Confirmation: The Orchestrator sends the final results or updates to the user, completing the workflow.

Why This Workflow Matters

This end-to-end workflow showcases the modularity and efficiency of the Pantheon (EON) ecosystem:

  • Seamless Integration: Orchestrates Tools, Agents, and Memory Systems effectively.

  • Scalability: Handles large-scale, concurrent workflows using distributed architecture.

  • Adaptability: Dynamically adapts workflows to changing data and requirements.

Explore Further

Orchestrators

Understand the role of Orchestrators in Pantheon

Agents

Dive into how Agents execute workflows

Tools

Explore the atomic building blocks of Pantheon

Projects

See how workflows are executed as Projects

Pantheon (EON) E2E AI Workflow