SYSTEM ONLINE: v2.0.0 (Universal)

FoxOps Autonomy

Bridging AI Reasoning with Universal Execution. An idempotent, self-healing engine that turns silent failures into auditable assets.

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Executive Summary

FoxOps' Autonomous Self-Healing Infrastructure is an agentic engine that turns silent failures into auditable assets. It uses Make.com as a Serverless MCP Host to give Gemini 2.0 direct control over industrial infrastructure, executing fixes and generating forensic reports without human intervention.

Objective

Autonomous Remediation

Engine

Gemini 2.0 + Vector Search

Output

Forensic HTML Reports

Universal Error Intelligence

FoxOps operates as a central nervous system, utilizing deterministic AI Signatures to handle distinct failure classes with a single engine.

Domain	Error Signature	Auto-Heal Strategy
🏭 Industrial	Servo Overheat (Temp > 85°C)	Trigger Cool-down & Recalibration SOP
🤖 RPA Bots	SelectorNotFound / Timeout	Execute Fallback DOM Logic
☁️ DevOps	OOM Kill / 503 Unavailable	Restart Service / Scale Pods

The Downtime Gap

The Problem

The "Black Hole" of Downtime

In industrial automation, downtime costs $10k/minute. When a technician fixes a sensor but doesn't log it, that knowledge is lost forever.

✕ Ghost Errors (No logs, no history)
✕ Human Bottlenecks at 3 AM
✕ Fragile JSON handling in legacy systems

> CRITICAL_ALERT: Valve_Stiction_Detected
> HUMAN_RESPONSE_TIME: 45 Minutes
> REVENUE_LOST: $450,000

The FoxOps Solution

If it's not documented, it didn't happen.

We built a "Senior Architect" engine that doesn't just fix the machine—it writes the paperwork. FoxOps detects, analyzes, and heals itself before a human is even notified.

✓

Self-Healing: Vector-matched SOP execution via Supabase.
✓

Teaching: Gemini drafts new SOPs for unknown errors.
✓

Forensics: Automated HTML/PDF Post-Mortem reports.

● FOXOPS_RESPONSE_TIME: 1.2s

Architecture & Stack

The system operates on a 4-Lane Framework, utilizing Supabase as the "Hard Drive" for knowledge (SOPs) and Make as the "Motherboard" for orchestration.

Make

Orchestration

Supabase

Vector DB

Gemini

Reasoning Core

SvelteKit

Interface

System Architecture v1.0

SOURCE: Chart is crafted in Mermaid.jsRENDER: Vectorscope (Inverted)

4-Layer "Omnichannel" Ingestion

Human Layer

Support Portal

Operator reports "Vibration" via UI.

Logic Layer

Workflow Handler

Make scenario fails (API 429).

Physical Layer

IoT Sensors

Festo Pressure < 4.0 bar.

Vital Layer

Health Pings

Cron job detects Latency > 200ms.

MCP Integration

Unlike standard chatbots, FoxOps exposes Make scenarios as MCP Tools. This allows the AI to "reach out" into the physical world to perform diagnostics.

tool_definition.json Valid JSON

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Initiate Triage Arguments",
  "type": "object",
  "properties": {
    "search_query": {
      "type": "string",
      "description": "A precise, semantic search query derived from the raw telemetry."
    },
    "clean_title": {
      "type": "string",
      "description": "A standardized, professional title for the incident report."
    },
    "priority": {
      "type": "string",
      "enum": [
        "LOW",
        "MEDIUM",
        "HIGH",
        "CRITICAL"
      ],
      "description": "Severity level of the incident."
    },
    "detected_domain": {
      "type": "string",
      "description": "The suspected origin system (e.g., 'Stripe', 'Supabase', 'AWS')."
    },
    "logic_reasoning": {
      "type": "string",
      "description": "Brief technical justification for why this priority level was assigned."
    }
  },
  "required": [
    "search_query",
    "clean_title",
    "priority",
    "logic_reasoning"
  ],
  "additionalProperties": false
}

Core Schema

We utilize a tri-fold database structure: Incidents for high-velocity ingestion, SOPs for vector retention, and Tickets for human workflow.

table: incidents (The "Live Stream") High Velocity

id uuid (PK)

raw_payload jsonb

status varchar (active, resolved)

priority varchar (critical, low)

sop_match_id uuid (FK: sops.id)

table: sops (The "Hard Drive") Vector Store

id uuid (PK)

embedding vector(1536)

title text

workflow_description text (Context)

table: support_tickets (The "Human Queue") Manual Ops

ticket_id uuid (PK)

incident_id uuid (FK: incidents.id)

assigned_technician text

resolution_notes text

REAL-WORLD USE CASE

Self-Healing RPA Bots

When a vendor changes a button ID, RPA bots crash. FoxOps detects the ElementNotFound error, matches it to a fallback SOP, and injects a new XPath selector in real-time.

0.8s

Resolution Time

Stress Test Scenarios

We configured the Command Deck to inject four distinct classes of failure, testing the system's ability to handle IT, OT, and Security incidents simultaneously.

Physical Layer

Pneumatic Pressure Drop

Simulates a Festo CP Factory sensor reporting 3.2 bar (below 4.0 threshold). The engine must identify the hardware fault and trigger a "Maintenance Stop" SOP.

> ERR_CODE: P_LOW_CRITICAL_01

Integration Layer

API Rate Limit Burst

Floods the system with OpenAI requests to trigger a `429 Too Many Requests`. The engine must catch the error and implement an exponential backoff strategy.

> HTTP 429: RETRY_AFTER_20S

Security Layer

Auth Token Expiry

Injects a "403 Forbidden" error during a user password reset flow. Tests the system's ability to distinguish between a hack attempt and a valid support ticket.

> 403_FORBIDDEN_ACCESS

Infra Layer

Service Availability (503)

Simulates a total outage of the Public API gateway. The engine must route traffic to a fallback node or notify stakeholders immediately.

> 503_SERVICE_UNAVAILABLE

FoxOps' Autonomous Self-Healing Infrastructure & SOP Generation