🚀 AI Autonomous Operations Manager (n8n-Based)

🧠 Overview

This project is a multi-agent AI system built with n8n that autonomously manages internal operations.

It simulates an intelligent operations manager capable of:

• analyzing incoming requests
• prioritizing tasks based on business impact
• executing actions in real time
• storing and tracking tasks

The system combines:

• LLMs (via Groq)
• workflow automation
• modular agent-based architecture

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⚙️ System Architecture

The system is composed of independent workflows (agents) orchestrated by a main workflow:

Main Workflow (Orchestrator) ├── Interpreter Agent ├── Prioritization Agent ├── Task Manager Agent └── Memory Writer

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🔹 1. Main Workflow (Orchestrator)

Handles incoming requests via webhook and coordinates all agents.

Flow:

1. Receives request
2. Sends to Interpreter Agent
3. Sends output to Prioritization Agent
4. Executes Task Manager
5. Stores task in Memory Layer
6. Returns structured API response

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🤖 2. Interpreter Agent

Uses Groq LLM to analyze incoming text and convert it into structured data.

Responsibilities:

• Extract intent
• Detect category (technical, billing, etc.)
• Determine urgency
• Generate summary
• Decide if action is required

Output Example:

{
  "intent": "report_issue",
  "category": "technical",
  "urgency": "critical",
  "summary": "Database outage",
  "requires_action": true
}

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⚖️ 3. Prioritization Agent

Applies SLA-based business logic + AI refinement using Groq.

SLA Rules:

• P1 → 15 minutes
• P2 → 60 minutes
• P3 → 240 minutes
• P4 → 1440 minutes

Features:

• Impact calculation
• Rule-based prioritization
• AI refinement with strict constraints

Output Example:

{
  "priority": 1,
  "priority_label": "P1 - Critical",
  "sla_minutes": 15,
  "impact": "high",
  "reasoning": "System outage affecting multiple users"
}

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⚡ 4. Task Manager Agent

Executes real actions based on priority.

Responsibilities:

• Route tasks based on priority
• Send real-time alerts via Slack
• Format operational messages

Behavior:

• P1 → 🚨 Critical alert
• P2 → ⚠️ High priority notification
• P3 → 📝 Task queued
• P4 → 📦 Low priority backlog

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🗄️ 5. Memory Layer (Task Tracking)

Stores tasks in Google Sheets as a lightweight database.

Stored Data:

• Task ID
• User
• Summary
• Priority
• Status
• Timestamps

Example:

{
  "id": "TCK-171234567",
  "user": "support_team",
  "summary": "API failure",
  "priority": "P2 - High",
  "status": "open"
}

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🧪 Example Use Cases

• Incident management
• Internal request automation
• Customer support triage
• Operations monitoring
• AI-driven ticketing systems

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🧱 Tech Stack

• n8n – workflow orchestration
• Groq – LLM inference
• Slack – notifications
• Google Sheets – memory layer

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🚀 Key Features

• 🧠 Multi-agent AI architecture
• ⚖️ SLA-based prioritization
• ⚡ Real-time execution
• 🗄️ Persistent memory layer
• 🔌 API-first design
• 🔄 Fully modular workflows

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👨‍💻 Author

Built by Steve — focused on AI systems, automation, and building the future 🚀

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Interface Images