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
  • Steps to Build a Project
  • 1. Define the Objective
  • 2. Select Components
  • 3. Define Workflow
  • 4. Integrate Memory Systems
  • 5. Configure Inputs and Outputs
  • 6. Test and Validate
  • 7. Deploy the Project
  • Why Building Projects Matters
  • Explore Further
  1. The Pantheon (EON) Core
  2. Projects

Building Projects

Building Projects in the Pantheon (EON) ecosystem involves designing workflows that seamlessly integrate tools, agents, and memory systems. These workflows are defined as Directed Acyclic Graphs (DAGs), ensuring modularity, scalability, and adaptability. This guide provides a step-by-step overview of how to create and deploy Projects to achieve AI-driven solutions efficiently.

Steps to Build a Project

1. Define the Objective

Begin by specifying the high-level goal of the Project, such as:

  • Sentiment analysis of customer feedback.

  • Real-time financial data aggregation and visualization.

  • Automated marketing campaign generation.

Clear objectives guide the selection of tools, agents, and memory systems.

2. Select Components

Identify and select the components required for the Project:

  • Tools: Choose atomic functionalities for specific tasks (e.g., data fetching, processing).

  • Agents: Include intelligent actors that combine tools with memory for complex operations.

  • Sub-Projects: Reuse existing workflows or nested Projects to reduce redundancy.

Using components from the Global AI Registry ensures efficiency and compatibility.

3. Define Workflow

Structure the workflow as a DAG using YAML or JSON:

  • Task Sequencing: Define the order in which tasks are executed.

  • Parallel Execution: Identify tasks that can run concurrently to optimize performance.

  • Dependencies: Specify dependencies between tasks to ensure proper execution flow.

A well-defined workflow ensures clarity and scalability.

4. Integrate Memory Systems

Incorporate memory systems to enhance the Project:

  • Shared Memory (Qdrant): Access global knowledge for semantic search and context retrieval.

  • Private Memory (LightRAG): Store project-specific or sensitive data for isolated use.

  • Contextual Queries: Define how memory is accessed during workflow execution.

Memory integration ensures that the Project is context-aware and adaptable.

5. Configure Inputs and Outputs

Set up input and output schemas for the Project:

  • Data Sources: Define sources like AWS Kinesis, Kafka, or static datasets.

  • Output Formats: Specify the format of the results (e.g., JSON, dashboards, files).

  • Error Handling: Include fallback mechanisms for incomplete or invalid inputs.

Clear input/output definitions improve interoperability and debugging.

6. Test and Validate

Before deploying the Project:

  • Unit Testing: Verify individual components (tools, agents, sub-projects).

  • Workflow Testing: Ensure the DAG functions as intended across all tasks.

  • Performance Testing: Validate scalability under different workloads.

Testing ensures reliability and performance in production environments.

7. Deploy the Project

Deploy the Project to the Pantheon (EON) ecosystem:

  • Resource Allocation: Use the Orchestrator to allocate resources dynamically.

  • Event Triggers: Enable real-time execution based on data ingestion or user interactions.

  • Monitoring: Track execution progress and resource usage via dashboards or logs.

Deployment turns the Project into an actionable, scalable solution.

Why Building Projects Matters

Building Projects allows users to:

  • Create scalable workflows tailored to their specific needs.

  • Reuse and integrate existing components for efficiency.

  • Leverage the full capabilities of the Pantheon (EON) ecosystem.

These benefits make Projects the foundation of AI-driven solutions.

Explore Further

PreviousProjectsNextSecurity & Configuration

Last updated 1 month ago

Security & Configuration

Learn how to secure and configure Projects for scalable deployments

Overview

Explore the role of knowledge layers in enhancing Projects