COO-Aufgabenpriorisierung

• •PostgreSQL-Datenbankverbindungsdaten
• •OpenAI API Key
• •Pinecone API Key

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        "content": "## 🔁 Section 1: Trigger & Data Retrieval\n\n### 🔹 **Node: `Daily 9AM Trigger`**\n\n* **Function**: Starts the workflow every day at 9:00 AM.\n* **Type**: Time-based scheduler.\n* **Why**: Ensures task processing is automated and consistent on a daily schedule.\n\n### 🔹 **Node: `Fetch Asana Tasks`**\n\n* **Function**: Retrieves all tasks from Asana that are due or assigned.\n* **Type**: API Connector to Asana.\n* **Why**: Acts as the entry point for incoming task data that needs prioritization.\n"
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        "content": "## 🧠 Section 2: AI Prioritization\n\n### 🔹 **Node: `COO Task Prioritization Agent`**\n\n* **Function**: Uses an AI Agent to evaluate and assign a priority to each task.\n* **Inputs**: Raw task data from Asana.\n* **Outputs**:\n\n  * `task_name`\n  * `priority_level` (e.g., High, Medium, Low)\n  * `reason_for_priority` (a justification provided by the AI)\n* **Why**: Simulates a COO’s judgment to focus on strategic and urgent tasks.\n\n### 🔹 **Node: `Task Reasoning Model`**\n\n* **Function**: Enhances the AI Agent's decision-making by providing natural language reasoning.\n* **Model**: OpenAI Chat Model.\n* **Why**: Adds contextual understanding to why a task is important.\n\n### 🔹 **Node: `Priority Output Parser`**\n\n* **Function**: Extracts structured data (task name, priority level, reasoning) from the AI’s output.\n* **Why**: Ensures only clean, usable data is passed to the database layer."
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        "content": "## 🗃️ Section 3: Data Storage & Enrichment\n\n### 🔹 **Node: `Save Prioritized Tasks`**\n\n* **Function**: Inserts each prioritized task into a PostgreSQL database.\n* **Fields**:\n\n  * `task_name`\n  * `priority_level`\n  * `reason_for_priority`\n* **Why**: Provides a persistent store for further reporting, monitoring, or automation."
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        "content": "=======================================\n            WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n    Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n   - YouTube: https://www.youtube.com/@YaronBeen/videos\n   - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n"
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        "content": "Absolutely! Here's a **very detailed, polished explanation** of your workflow — structured in a **clean, professional format** with icons and broken into **three logical sections**. This is suitable for documentation, presentations, or onboarding team members.\n\n---\n\n# 🧠 COO Task Automation Workflow Documentation\n\nThis intelligent automation workflow simulates a **COO’s decision-making** by prioritizing daily tasks fetched from Asana using AI. It operates autonomously every morning at 9 AM, analyzes the tasks using an AI reasoning engine with memory and context, and stores the prioritized outcomes in a structured database for operational use.\n\n---\n\n## ⏰ 1. Trigger & Task Collection\n\n### 📅 **Automated Daily Start**\n\n* **Time-based Scheduling**: The system is configured to initiate **automatically every day at 9:00 AM** without any manual input.\n* This guarantees consistency in daily planning and ensures tasks are prioritized early in the workday.\n\n### 🔗 **Integration with Asana**\n\n* The workflow connects directly to the **Asana task management platform**.\n* It fetches tasks assigned to the team or specific users, filtered by criteria such as due date, project, or tag.\n* These tasks include metadata like task name, description, due date, assignee, and any custom fields.\n* All this information becomes the **raw input** for the AI decision-making engine.\n\n---\n\n## 🧠 2. Intelligent Task Prioritization\n\n### 🤖 **AI-Powered Prioritization Logic**\n\n* Once tasks are collected, they are passed to a custom **AI Agent**.\n* This AI agent plays the role of a **virtual COO**, applying strategic reasoning to prioritize tasks based on business urgency, impact, and context.\n\n### 🧩 **Reasoning Through Language Models**\n\n* Behind the scenes, the agent leverages a **large language model (LLM)** to:\n\n  * Analyze each task's content.\n  * Cross-reference it with similar past tasks or organizational priorities.\n  * Generate an intelligent output:\n\n    * **Task Name**\n    * **Priority Level** (High, Medium, Low)\n    * **Reason for Priority** (justification written in natural language).\n\n### 🗂️ **Structured Output Conversion**\n\n* Since the AI outputs natural language, the system uses a **parser** to extract structured fields.\n* This step ensures that the response is normalized and can be cleanly inserted into a relational database.\n\n---\n\n## 📦 3. Enrichment & Persistent Storage\n\n### 🧾 **Saving to PostgreSQL**\n\n* The structured data is stored in a **PostgreSQL database** with the following schema:\n\n  * `task_name`: The name or summary of the task.\n  * `priority_level`: Categorized as High, Medium, or Low.\n  * `reason_for_priority`: A brief but clear explanation of why the task received this priority.\n\n### 📚 **Contextual Intelligence with Vector Embeddings**\n\n* To make future decisions more intelligent, the system incorporates a **semantic memory layer** using **vector databases** and **embeddings**.\n* All task-related text is converted into **numerical vector representations** using an embeddings model.\n* These embeddings are stored in a **vector database**, enabling the AI agent to:\n\n  * Recall similar past tasks.\n  * Understand historical context.\n  * Find patterns across time and projects.\n\n### 🔍 **Semantic Search & Retrieval**\n\n* When making prioritization decisions, the AI queries the vector database to retrieve **semantically similar tasks**.\n* This enables **context-aware decision-making**, allowing the AI to make better judgments based on past learnings, not just current inputs."
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