Megasus

Megasus - Autonomous Industrial Sentinel Network: The Self-Healing Enterprise. A Gemini 3 agent swarm that autonomously detects faults, negotiates procurement, and dispatches repairs in real-time.

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Inspiration

The inspiration for Megasus Autonomous Industrial Self-Healing Network came from a critical realization: while industrial visibility is solved, industrial velocity is not. We have sensors everywhere, IoT dashboards blinking green and red but when a critical failure occurs, the response is archaic. An operator sees a red light, scrambles for a manual, calls a vendor, waits on hold, and manually logs a ticket.

In the era of Gemini 3, this latency is unacceptable. I wanted to move beyond "monitoring" to "acting." I asked myself: What if the machine didn't just cry for help, but actually fixed itself? What if a thermal runaway event could trigger an autonomous swarm of agents that not only safed the machine but negotiated the purchase of a replacement part and dispatched a technician before a human operator even poured their coffee?

This project is my answer to the Action Era of AI. It transitions from static chat interfaces to a living, breathing, self-healing industrial ecosystem.

What it does

Megasus Autonomous Industrial Self-Healing Network (A.I.S.N.) is a dual-mode industrial autonomy platform that manages critical assets, specifically demonstrating a high-fidelity digital twin of a Heavy Duty Mining Excavator.

In Mode A (Human Oversight), the system functions as a traditional, high-end IoT dashboard. It visualizes real-time telemetry (RPM, Torque, Core Temp), streams data via WebSockets, and alerts the operator to faults like Thermal Runaway. However, resolution remains manual.

In Mode B (Autonomous Swarm), the system engages a multi-agent swarm powered by Gemini 3 Pro. When a critical fault occurs:

Safety Interlock: The Primary Agent (Marathon Agent) instantly detects the spike and sends a physical IoT command to "DERATE" the engine, cooling it down and preventing catastrophic failure.

Visual Verification: The agent virtually inspects the camera feed to confirm smoke or hazards.

Autonomous Procurement: Realizing the inventory for the specific inverter part is empty, the Primary Agent spins up a secondary Vendor Agent. They verbally negotiate price and availability in real-time.

System of Record Updates: Once the deal is closed (within variance limits), the swarm autonomously updates the ERP (Inventory), CRM (Ticket), and FSM (Field Service technician Dispatch) databases.

The entire process, from detection to dispatch happens in under 10 seconds, with full transparency provided via a "Glass Box" terminal that visualizes and vocalizes the agent negotiation.

How we built it

I built Megasus A.I.S.N. as a full-stack, event-driven architecture designed for low latency and high reliability.

The Backend (Brain & Physics):

I used FastAPI on Google Cloud Run to host the core logic.

Physics Engine: I wrote a custom simulation class in Python that generates realistic telemetry curves for torque, rpm, and temperature, creating dynamic failure states (Thermal Runaway) based on stochastic inputs.

Agent Swarm: I utilized the Google GenAI SDK to orchestrate gemini-3-pro-preview. The architecture uses a "Marathon Agent" pattern where the LLM has access to specific tools (send_iot_command, check_inventory, negotiate).

Database: I deployed a PostgreSQL instance on Cloud SQL to act as the single source of truth for the simulated ERP, CRM, and FSM systems. I used asyncpg with a singleton connection pool to handle the high-frequency writes required by the agent swarm.

The Frontend (Command Center):

I built the dashboard using Next.js 16 and React.

3D Visualization: I integrated Three.js (via React Three Fiber) to render a 3D model of the excavator that responds to the backend state—rotating when active and stopping during faults.

Real-Time Comms: I implemented a robust WebSocket layer that streams telemetry at 60Hz and pipes the agent's internal "thought process" directly to the UI.

Audio Synthesis: To make the "Action Era" tangible, I used the Gemini Live API for the Human-To-Agent interaction and Web Speech API to give unique voices to the Safety Agent and the Vendor Agent, allowing users to hear the negotiation live.

Deployment:

The entire stack is containerized with Docker and deployed via Google Cloud Build directly to Cloud Run, ensuring a serverless, scalable environment.

Challenges we ran into

The biggest challenge was Database Connection Exhaustion during the multi-agent execution.

When the "Autonomous Mode" kicked in, the frontend was polling for updates while the agents were simultaneously hammering the database to check inventory, create tickets, and log dispatch orders in parallel. This caused a "too many clients" error that crashed the backend. I solved this by implementing a rigid Singleton Connection Pool pattern in Python and optimizing the frontend polling intervals to allow the database to breathe during high-concurrency events.

Another hurdle was Latency in Agent Handoffs.

Initially, having one agent call another introduced a noticeable delay. I optimized this by refining the system prompts to be extremely terse and action-oriented, leveraging the speed of gemini-3-pro-preview to reduce the "time-to-first-token" and make the voice conversation feel natural rather than robotic.

Accomplishments that we're proud of

I am most proud of the "Zero-Latency" user experience. Seeing the system go from a red "CRITICAL" state to a stable "DERATED" state with a technician dispatched in under 10 seconds without touching a single button feels like magic.

I am also proud of the Multi-Agent Negotiation. It is not just a hardcoded script; the agents actually reason over the price. If the simulated vendor sets the price too high, the Marathon agent rejects it. Watching two AI agents haggle over an industrial part while I sat back and watched was a genuine "the future is here" moment.

What we learned

I learned that Context is King but Action is Emperor. Gemini 3 Pro is incredibly capable at reasoning, but its true power unlocks when you give it dangerous tools—like the ability to write to a database or control a machine—and trust it to execute.

I also learned the importance of "Glass Box" AI. In industrial settings, you cannot have a Magic Box making decisions. By streaming the agent's thought logs and vocalizing the negotiation, I built trust into the system. The operator knows exactly why the AI ordered the part, which is crucial for adoption.

What's next for Megasus

The next step for the Megasus/A.I.S.N. platform is multimodal sensory integration. I plan to enable the agents to ingest live audio spectra from the machine to detect bearing failures before they happen (acoustic monitoring) and use Gemini 3's video understanding to visually inspect the site for safety compliance (e.g., detecting if a worker is too close to the active machinery).

We are moving from "Self-Healing" to "Pre-Cognitive" operations—fixing the machine before it even knows it's broken.

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