🦊 FoxOps' Autonomous Self-Healing Infrastructure & SOP Generation

"If it’s not documented, it didn’t happen."

The "Context Healer" for Fabalos OS. Bridging AI Reasoning (Gemini 2.0) with Industrial Execution (Make + MCP) to turn silent failures into auditable assets.

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1. Executive Summary

FoxOps is a universal autonomous incident response system designed to detect, analyze, and resolve technical failures across ANY domain—from industrial sensors and RPA bots to API gateways and Cloud infrastructure.

Unlike passive logging tools, FoxOps actively executes "Self-Healing Cycles". It uses Vector Search to match error signatures against a knowledge base of Standard Operating Procedures (SOPs), executes the fix via Make.com, and auto-generates forensic documentation.

The Core Philosophy: The system doesn't care where the error comes from. If it generates an error message, FoxOps can triage it, fix it, and document it.

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2. System Architecture

The architecture follows a strict "Observe-Orient-Decide-Act" (OODA) loop, leveraging Edge Computing for sanitization and Agentic AI for reasoning.

🧠 The Intelligence Stack

• Ingestion (The Shield): Cloudflare Worker acts as the firewall, sanitizing raw logs into deterministic AI Signatures (e.g., converting 0x4F5A → {HEX}) to ensure Idempotency.

• The Brain (Reasoning): Gemini 2.0 Flash handles high-speed triage (0.4s latency), while Gemini 1.5 Pro handles deep forensic analysis and SOP generation.

• The Memory (Context): Supabase Vector stores SOP embeddings, allowing the system to recall fixes for similar incidents ("Semantic Recall").

• The Hands (Action): Make.com acts as the orchestration bus, connecting the brain to 500+ services (Slack, Jira, AWS, Email).

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3. Key Features

🛡️ Deterministic "AI Signature" Normalization

To prevent "LLM Hallucinations" and ensure consistent vector matching, FoxOps strips variable data (IPs, Timestamps, Memory Addresses) at the edge.

• Raw Input: Error: Connection timeout at 192.168.1.55

• FoxOps Signature: Error: Connection timeout at {IP}

• Result: 100% Consistent SOP Retrieval.

🔄 The Self-Healing Loop

1. Detect: Log arrives via Webhook.

2. Search: System queries Vector DB for an existing SOP.

3. Execute: If found, the SOP (e.g., "Restart Service") is executed automatically.

4. Learn: If NOT found, Gemini Pro researches the error, drafts a new SOP, and saves it to the database for next time.

📄 Instant Forensic HTML Reporting

FoxOps doesn't just fix the issue; it proves it. Every incident generates an immutable Forensic Report containing:

• Timestamp & Severity

• Tech Stack Trace

• Resolution Actions

• HTML-formatted layout ready for Email/PDF.

4. Universal Error Intelligence

FoxOps operates as a central nervous system for technical infrastructure, handling distinct failure classes with a single engine:

Domain	Error Type	Auto-Heal Strategy
🏭 Industrial	Pressure < 4.0 bar	Trigger Maintenance Stop SOP
🤖 RPA Bots	ElementNotFound	Execute Fallback Selector Logic
☁️ DevOps	503 Service Unavailable	Restart Service / Scale Resources
🔐 Security	403 Unauthorized	Revoke Token & Log Incident
💳 FinTech	Webhook Sig Mismatch	Request Re-signing / Alert Ops

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5. Technical Stack

Layer	Technology	Function
Edge Compute	Cloudflare Workers	Traffic Sanitization & Pre-processing
Frontend	SvelteKit (Vercel)	Real-time Dashboard & PDF Rendering
Orchestration	Make.com	Master Scenario & Logic Routing
Database	Supabase	PostgreSQL + pgvector (Knowledge Base)
Auth	Supabase Auth	Row Level Security (RLS) & Server Hooks
Intelligence	Gemini 1.5 Pro	Reasoning Agent via Vertex AI

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6. Data Flow Architecture

The system operates on a linear, idempotent pipeline designed to handle "dirty" industrial data without crashing.

Phase A: Ingestion & Edge Sanitization

1. IoT Trigger: Edge devices send raw error logs (often containing control characters, CRLF, and unescaped quotes).
2. 🛡️ Cloudflare Sentinel (Pre-processing):
   • Incoming webhooks hit a dedicated Cloudflare Worker before reaching the orchestration layer.
   • Action: Strips unwanted metadata, blocks malformed payloads, and sanitizes headers to ensure only valid JSON enters the pipeline.
3. Transport Layer Normalization:
   • Validated payloads are passed to the Make.com Native JSON Serialization Module.
   • Purpose: Automatically escapes \n, \t, and " to prevent JSON syntax violations (RFC 8259 compliance).

Phase B: Logic & Analysis

1. Vector Search: The error signature is embedded and queried against the knowledge_base in Supabase.
2. Decision Engine:
   • Match Found (>0.85): Trigger Self-Healing (Green Lane).
   • No Match: Trigger Analyst Escalation (Red Lane).
3. Deterministic Transformation (Code Node):
   • If SOP Title is null (New Incident), the system performs a logic fallback to use Ticket Title.
   • Generates a Forensic HTML Report string server-side.

Phase C: Persistence & Lifecycle

1. Active Operations (public.incidents):
   • Stores live/ongoing issues. Optimized for high-speed dashboard queries via Supabase Realtime.
2. Compliance Archival (public.incident_reports):
   • Trigger: Only when Status = RESOLVED.
   • The finalized HTML report and SOP snapshot are committed to cold storage.
   • Provides immutable audit trails for regulatory compliance.

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7. 4-Layer "Omnichannel" Ingestion

FoxOps doesn't just listen to one stream. It monitors the entire operational reality across four dimensions:

Dimension	Entry Point	Example Scenario
👤 Human Layer	Support Portal	Operator reports "Conveyor belt vibration" via web UI.
🧠 Logic Layer	Workflow Handler	Make.com scenario fails due to API Rate Limit (429).
🏭 Physical Layer	IoT Sensors	Festo Pressure Sensor drops below 4.0 bar critical threshold.
💓 Vital Layer	Health Pings	Cron job detects Supabase Database latency > 200ms.

Result: Whether it's a broken machine, a confused user, a crashed script, or a network outage—FoxOps catches it.

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8. Key Technical Features

🛡️ Edge-Layer Data Scrubbing (Cloudflare)

To protect the automation engine from "Garbage In, Garbage Out," a Cloudflare Worker acts as the gatekeeper. It intercepts raw TCP streams from industrial controllers, removing non-compliant characters and enforcing schema validation before the data ever consumes a Make.com operation.

⚡ Transport-Layer Integrity

Implemented a "No-Code" Native JSON serializer to handle multi-line mechatronic SOPs. This eliminates "Bad Control Character" errors common in legacy industrial data, ensuring 100% payload delivery to the frontend.

📄 Server-Side Forensic Reporting

The system generates a styled, legal-grade HTML report within the orchestration layer.

• Benefit: The SvelteKit frontend simply renders the HTML string ({@html incident.html_report}).
• Output: Ready-to-print PDF format with timestamps, severity levels, and validation hashes.

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9. API Payload Specification

The SvelteKit frontend receives the following standardized JSON structure via the Webhook Response:

{
  "tenant": {
    "id": "FAB-IND-01",
    "name": "Festo Industrial Plant"
  },
  "user": {
    "email": "demo+festo@fabalos.com"
  },
  "incident": {
    "id": "uuid-v4",
    "title": "Actuator Cycle Timeout - Station 04",
    "status": "⚡ ACTIVE: SELF-HEALING CYCLE IN PROGRESS",
    "priority": "MEDIUM",
    "raw_error": "E-2044 // LATENCY_DETECTED...",
    "sop_title": "Troubleshooting Incomplete Pneumatic Stroke...",
    "sop_procedure": "Step 1: Apply LOTO...\nStep 2: Inspect S3...",
    "html_report": "<div style='...'><h1>CRITICAL INCIDENT REPORT</h1>...</div>",
    "generated_at": "2026-01-27T10:00:00.000Z"
  }
}

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10. Deployment

Prerequisites

• Supabase Project with vector extension enabled.

• Google Vertex AI / Gemini API Key.

• Make.com Account.

Quick Start

Bash

# 1. Clone the Repository
git clone https://github.com/fabalos/foxops-engine.git

# 2. Deploy Edge Worker
npx wrangler deploy

# 3. Configure Make Webhook
# Point your application logging driver to the Cloudflare Worker URL.

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Part of the Master of Make 2026 Submission. Architected by Fabalos.