{ "id": "czDg0Q7wsAoqYQIx", "name": "COO Task Prioritization", "tags": [], "nodes": [ { "id": "39d955ab-7c8e-4faa-ae3d-948dfa5e0615", "name": "Pinecone Vector Store", "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone", "position": [ 640, 500 ], "parameters": { "options": {}, "pineconeIndex": { "__rl": true, "mode": "list", "value": "" } }, "credentials": { "pineconeApi": { "name": "" } }, "typeVersion": 1.1 }, { "id": "923a5e54-785a-4c4f-b6b8-f540bb1c4e19", "name": "Daily 9AM Trigger", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 0, 0 ], "parameters": { "rule": { "interval": [ { "triggerAtHour": 9 } ] } }, "typeVersion": 1.2 }, { "id": "28adcd8d-e4cf-45f7-ad32-16ffe30a525b", "name": "Fetch Tasks", "type": "n8n-nodes-base.asana", "position": [ 220, 0 ], "parameters": { "filters": { "project": "" }, "operation": "getAll", "returnAll": true }, "typeVersion": 1 }, { "id": "b43946e1-1b62-46fc-9211-72551b6c55c7", "name": "COO Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 660, 0 ], "parameters": { "text": "={{ $json.data }}", "options": { "systemMessage": "=You are a COO AI agent that helps prioritize operational tasks. Use the retrieved context from vector_database_tool to assess task urgency and strategic importance.\n\nFor the most priority task, output:\n\nTask Name\n\nPriority Level: High / Medium / Low\n\nDue Date\n\nReason for Priority\n\nConsider urgency (due date), impact on operations, and dependencies retrieved from vector knowledge.\nJust give back the most important task" }, "promptType": "define", "hasOutputParser": true }, "typeVersion": 1.9 }, { "id": "b716a7b4-0357-4730-89b2-8bc295d5a22a", "name": "Save Prioritized Task", "type": "n8n-nodes-base.postgres", "position": [ 1600, 0 ], "parameters": { "table": { "__rl": true, "mode": "list", "value": "prioritized_tasks", "cachedResultName": "prioritized_tasks" }, "schema": { "__rl": true, "mode": "list", "value": "public" }, "columns": { "value": { "task_name": "={{ $json.output.tasks[0][\"Task Name\"] }}", "priority_level": "={{ $json.output.tasks[0][\"Priority Level\"] }}", "reason_for_priority": "={{ $json.output.tasks[0][\"Reason for Priority\"] }}" }, "schema": [ { "id": "id", "type": "number", "display": true, "removed": true, "required": false, "displayName": "id", "defaultMatch": true, "canBeUsedToMatch": true }, { "id": "task_name", "type": "string", "display": true, "required": true, "displayName": "task_name", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "priority_level", "type": "string", "display": true, "required": false, "displayName": "priority_level", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "reason_for_priority", "type": "string", "display": true, "required": false, "displayName": "reason_for_priority", "defaultMatch": false, "canBeUsedToMatch": true } ], "mappingMode": "defineBelow", "matchingColumns": [ "id" ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "credentials": { "postgres": { "name": "" } }, "typeVersion": 2.6 }, { "id": "759f799a-d046-4609-8894-99a9b951e43f", "name": "Task Reasoning Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 560, 260 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini" }, "options": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.2 }, { "id": "4e3a60ec-d900-44e9-beaf-0de5905d9b2e", "name": "Priority Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "position": [ 1120, 180 ], "parameters": { "jsonSchemaExample": "{\n \"tasks\": [\n {\n \"Task Name\": \"Review quarterly KPIs\",\n \"Priority Level\": \"High\",\n \"Reason for Priority\": \"Provides critical performance metrics for all departments; deadline is approaching.\"\n },\n" }, "typeVersion": 1.2 }, { "id": "d7ae1305-e2f2-44c0-9c21-8cb29310a5d8", "name": "Task Knowledge Vector DB", "type": "@n8n/n8n-nodes-langchain.toolVectorStore", "position": [ 820, 220 ], "parameters": { "name": "vector_database_tool", "description": "The information related to task prioritization is here" }, "typeVersion": 1 }, { "id": "906b83fc-d501-4442-b470-3e90148ace33", "name": "Vector Query Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 960, 440 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini" }, "options": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.2 }, { "id": "e2b8e43c-1f7e-43e0-bba1-d186776d1e2b", "name": "Generate Task Embeddings", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [ 580, 720 ], "parameters": { "options": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.2 }, { "id": "61c92166-6068-4884-ac39-af52ae4a496a", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -40, -480 ], "parameters": { "color": 6, "width": 400, "height": 680, "content": "## \ud83d\udd01 Section 1: Trigger & Data Retrieval\n\n### \ud83d\udd39 **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### \ud83d\udd39 **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" }, "typeVersion": 1 }, { "id": "c9d36c9c-2cd3-407c-bc97-c765e6cb3bc6", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 500, -600 ], "parameters": { "color": 5, "width": 760, "height": 1480, "content": "## \ud83e\udde0 Section 2: AI Prioritization\n\n### \ud83d\udd39 **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\u2019s judgment to focus on strategic and urgent tasks.\n\n### \ud83d\udd39 **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### \ud83d\udd39 **Node: `Priority Output Parser`**\n\n* **Function**: Extracts structured data (task name, priority level, reasoning) from the AI\u2019s output.\n* **Why**: Ensures only clean, usable data is passed to the database layer." }, "typeVersion": 1 }, { "id": "4030c4f7-140c-453b-8441-4ed230da0928", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 1460, -460 ], "parameters": { "color": 3, "width": 380, "height": 640, "content": "## \ud83d\uddc3\ufe0f Section 3: Data Storage & Enrichment\n\n### \ud83d\udd39 **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." }, "typeVersion": 1 }, { "id": "cd768682-0aee-41d9-bdbc-0b3e8509a042", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ -1980, -460 ], "parameters": { "color": 4, "width": 1300, "height": 320, "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" }, "typeVersion": 1 }, { "id": "019cb716-597a-49b3-8eeb-fc523f210c8f", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ -1980, -120 ], "parameters": { "color": 4, "width": 1289, "height": 2238, "content": "Absolutely! Here's a **very detailed, polished explanation** of your workflow \u2014 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# \ud83e\udde0 COO Task Automation Workflow Documentation\n\nThis intelligent automation workflow simulates a **COO\u2019s 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## \u23f0 1. Trigger & Task Collection\n\n### \ud83d\udcc5 **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### \ud83d\udd17 **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## \ud83e\udde0 2. Intelligent Task Prioritization\n\n### \ud83e\udd16 **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### \ud83e\udde9 **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### \ud83d\uddc2\ufe0f **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## \ud83d\udce6 3. Enrichment & Persistent Storage\n\n### \ud83e\uddfe **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### \ud83d\udcda **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-YOUR_OPENAI_KEY_HERE 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### \ud83d\udd0d **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." }, "typeVersion": 1 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "8c201cdf-caaa-423e-ad38-f6ac7955ff32", "connections": { "COO Agent": { "main": [ [ { "node": "Save Prioritized Task", "type": "main", "index": 0 } ] ] }, "Fetch Tasks": { "main": [ [ { "node": "COO Agent", "type": "main", "index": 0 } ] ] }, "Daily 9AM Trigger": { "main": [ [ { "node": "Fetch Tasks", "type": "main", "index": 0 } ] ] }, "Vector Query Model": { "ai_languageModel": [ [ { "node": "Task Knowledge Vector DB", "type": "ai_languageModel", "index": 0 } ] ] }, "Task Reasoning Model": { "ai_languageModel": [ [ { "node": "COO Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Pinecone Vector Store": { "ai_vectorStore": [ [ { "node": "Task Knowledge Vector DB", "type": "ai_vectorStore", "index": 0 } ] ] }, "Priority Output Parser": { "ai_outputParser": [ [ { "node": "COO Agent", "type": "ai_outputParser", "index": 0 } ] ] }, "Generate Task Embeddings": { "ai_embedding": [ [ { "node": "Pinecone Vector Store", "type": "ai_embedding", "index": 0 } ] ] }, "Task Knowledge Vector DB": { "ai_tool": [ [ { "node": "COO Agent", "type": "ai_tool", "index": 0 } ] ] } } }