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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On this page
  • The Workflow Steps
  • 1. Submitting a Prompt
  • 2. Interpreting the Prompt
  • 3. Discovering Relevant Components
  • 4. Building the workflow
  • 5. Executing the Workflow
  • 6. Delivering Results
  • Why It Works
  • Explore Further
  1. The Pantheon (EON) Ecosystem
  2. User Journey

User Workflow: From Prompt to Project

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Last updated 1 month ago

Pantheon (EON) simplifies the process of transforming a high-level user prompt into a fully executable project by leveraging its modular and scalable architecture. This workflow showcases how the ecosystem streamlines each step, from interpreting the user’s intent to orchestrating the final solution.

The Workflow Steps

1. Submitting a Prompt

The journey begins when a user submits a high-level prompt, such as:

  • "Analyze customer sentiment from product reviews."

  • "Generate a meme campaign based on trending topics."

This prompt is sent to the Orchestrator, which serves as the central brain of the ecosystem.

2. Interpreting the Prompt

The Orchestrator interprets the user’s prompt and translates it into actionable tasks:

  • NLP Analysis: Break the prompt into smaller tasks.

  • Task Mapping: Determine the required tools, agents, and workflows to accomplish the goal.

For example, a sentiment analysis prompt might involve:

  1. Fetching data from social media (Tool).

  2. Analyzing text sentiment (Agent).

  3. Aggregating results into a report (Workflow).

3. Discovering Relevant Components

Once tasks are defined, the Orchestrator queries the Global AI Registry to find:

  • Tools: Atomic functionalities to perform each task.

  • Agents: Pre-trained intelligent actors that combine tools and memory.

  • Projects: Existing workflows that match the user’s objective.

This step ensures the most efficient components are used while leveraging reusable elements.

4. Building the workflow

The Orchestrator dynamically constructs a workflow from discovered components:

  • Directed Acyclic Graph (DAG): Tasks are organized into a structured execution flow.

  • Shared and Private Memory: Integrates relevant knowledge sources (Qdrant for global, LightRAG for private).

  • Event Listeners: Configures real-time triggers for dynamic data ingestion, if required.

5. Executing the Workflow

The workflow is executed seamlessly across the distributed infrastructure:

  • Ray Clusters handle task distribution and execution.

  • Autoscaling ensures resources are dynamically allocated based on workload demands.

  • Event-Driven Adaptation enables the system to respond to real-time inputs or errors.

Users can monitor progress via dashboards or APIs.

6. Delivering Results

Upon completion, the system delivers the results in the desired format:

  • Data Outputs: Dashboards, files, or API endpoints.

  • Knowledge Updates: Saves insights to shared or private memory for future use.

  • Deployment Feedback: Users can iterate on workflows based on performance metrics, voting, or new requirements.

This seamless end-to-end process ensures that users achieve their objectives efficiently.

Why It Works

The Pantheon (EON) workflow bridges the gap between user intent and actionable AI solutions. By automating complex processes and integrating modular components, it minimizes manual effort and maximizes scalability and adaptability.

Explore Further

Overview

Dive deeper into the core structure of Pantheon (EON)

Core Principles

Learn about the guiding values of the ecosystem

Pantheon (EON) User Workflow