Intelligence that Acts.

Autonomous Systems for Mission-Critical Environments.

The OptimumT Edge: You have reached the intersection of high-stakes engineering and Agentic AI. We bridge the “Autonomy Gap” by developing deterministic, self-correcting reasoning engines for UAV Swarms, Medical Device Software, and Industrial Robotics.

From edge-computed flight logic to real-time surgical tool detection, we don’t just build smart software—we build systems that can be trusted when failure is not an option.

While most AI focuses on generation, OptimumT focuses on verification. Our Agentic Framework utilizes a proprietary Reflector-Worker loop that ensures physical hardware never executes a command that violates safety envelopes. By synthesizing multi-modal sensor data in real-time, our agents build a dynamic world model that persists through GPS denial, signal interference, and hardware degradation.

At OptimumT, we believe the next frontier of AI isn’t a better chatbot—it is a more reliable robot. We are building the invisible infrastructure that allows machines to think, reason, and act with the same rigor as a human expert. Whether it’s navigating a subsea rig or assisting in a delicate spinal procedure, our goal is simple: Zero-latency autonomy with absolute safety.

from openagent import Agent, Worker, Reflector
import time
import random

# 1. Define the Sensor Data Source (Mock)
def get_oil_rig_sensor_data():
    # Simulating a PSI sensor from a subsea wellhead
    pressure = round(random.uniform(2500, 3200), 2)
    return {"sensor_id": "SR-X102", "pressure_psi": pressure, "timestamp": time.time()}

# 2. Define the Worker Agent
# Task: Ingest raw telemetry and format it for analysis.
worker_agent = Worker(
    role="Sensor Ingestor",
    goal="Fetch real-time pressure data from the oil rig and prepare it for safety review.",
    backstory="Expert in SCADA systems and industrial IoT telemetry.",
    allow_delegation=False
)

# 3. Define the Reflective Agent
# Task: Critically analyze the data for anomalies and safety breaches.
reflective_agent = Reflector(
    role="Safety Analyst",
    goal="Verify sensor data accuracy and identify potential safety risks or anomalies.",
    backstory="Senior Petroleum Engineer specializing in risk mitigation and anomaly detection.",
    feedback_threshold=0.8 # Only passes result if confidence is high
)

# 4. Orchestrate the Interaction
def run_autonomous_monitoring():
    print("--- Starting Agentic Monitoring Loop ---")
    
    # Worker fetches the data
    raw_data = get_oil_rig_sensor_data()
    print(f"[Worker] Ingested Data: {raw_data}")

    # Worker passes a draft report to the Reflective Agent
    initial_report = f"Sensor {raw_data['sensor_id']} reports {raw_data['pressure_psi']} PSI."
    
    # The Reflection Loop
    # The reflective agent reviews the worker's data and provides a 'Reflected' result
    final_output = reflective_agent.reflect(
        input_data=raw_data,
        worker_draft=initial_report,
        context="Current safety threshold is 3000 PSI. Any pressure above this requires an alert."
    )

    print("\n--- Final Safety Result ---")
    print(final_output)

if __name__ == "__main__":
    run_autonomous_monitoring()