Pyragogy AI Village - Orchestrazione Master (Architettura Profonda V2)

For Pyragogy AI Village, this automated workflow orchestrates a multi-agent system to efficiently process input data through various AI agents, ensuring optimal output via human review. It integrates with PostgreSQL for data management, utilizes OpenAI for intelligent processing, and supports email notifications and GitHub for content management. The workflow enhances collaboration, streamlines content creation, and ensures quality through human oversight, resulting in well-structured handbook entries ready for deployment.

7/4/2025
35 nodes
Complex
pyragogymulti-agentorchestrationhuman-in-loopwebhookcomplexstartpostgresqlopenaiemailsendwaitgithubslackrespondtowebhookadvancedintegrationapidatabasedatacodecustomlogicconditionalroutingemailnotificationcommunication
Categories:
Data Processing & AnalysisTechnical Infrastructure & DevOpsCommunication & MessagingComplex Workflow
Integrations:
StartPostgreSQLOpenAiEmailSendWaitGitHubSlackRespondToWebhook

Target Audience

  • Educators and Trainers: Those looking to enhance learning materials and resources through collaborative AI-driven processes.
    - Content Creators: Individuals who need a structured approach to generate, review, and archive content efficiently.
    - Researchers and Analysts: Professionals who require a method to synthesize and summarize large amounts of data and insights.
    - Project Managers: Leaders who want to streamline workflows involving multiple agents and human input for better project outcomes.
    - Technical Teams: Developers and engineers who are interested in integrating AI solutions into their existing systems for improved automation.

    • Problem Solved

    This workflow addresses the challenge of efficiently managing the content creation process by integrating various AI agents for summarization, synthesis, review, and archiving. It allows for seamless collaboration between AI and human reviewers, ensuring high-quality output while also facilitating feedback loops and revisions. The use of webhooks enables real-time processing and interaction, making it suitable for dynamic environments.

    Workflow Steps

  • Webhook Trigger: Initiates the workflow upon receiving a POST request with input data.
    2. Check DB Connection: Verifies the connection to the PostgreSQL database to ensure data can be stored and retrieved.
    3. Meta-Orchestrator: Analyzes the input and determines the optimal sequence of AI agents to process the data.
    4. Parse Orchestration Plan: Prepares the agent sequence for execution, setting up looping and initial input.
    5. Agent Execution: Each agent (e.g., Summarizer, Synthesizer) processes the data in the defined sequence, with outputs being passed along.
    6. Human Review: Generates a review request email, waits for human approval, and processes the decision (approved/rejected).
    7. Content Storage: If approved, saves the content to the database and logs contributions from each agent.
    8. GitHub Integration: Optionally commits the approved content to a GitHub repository for version control.
    9. Slack Notification: Sends a notification to a Slack channel upon workflow completion, summarizing the input and output.
    10. Final Response: Returns a structured JSON response with the final output and contributions to the original webhook request.

    • Customization Guide

    To customize this workflow, users can:
    - Modify Agent Sequences: Adjust the order of agents or add/remove agents based on specific needs or project requirements.
    - Change Database Configurations: Update PostgreSQL credentials and database tables to fit the user’s data structure and storage needs.
    - Alter Webhook Paths: Customize the webhook paths to better integrate with existing systems or applications.
    - Email and Slack Settings: Update email addresses and Slack webhook URLs to ensure notifications reach the appropriate users.
    - Feedback Mechanism: Tailor the feedback collection process from human reviewers to capture specific insights or comments relevant to the content being reviewed.