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Build OPC UA Remote Telemetry Diagnostics with OpenAI Agents SDK and OpenAI o3

Industrial remote monitoring systems struggle to pinpoint root causes when complex machinery telemetry degrades across connected OPC UA nodes. Build an autonomous telemetry agent using OpenAI Agents SDK and OpenAI o3 to diagnose SCADA signal anomalies, reducing mean time to detection below two seconds.

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What you are building

Develop an automated telemetry monitoring agent using OpenAI Agents SDK and OpenAI o3 that ingests OPC UA streams to detect and explain industrial equipment anomalies.

How work is evaluated

Evaluation

Evaluates the agent's accuracy in identifying telemetry root-causes and executing corrective PLC actions within time thresholds.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Diagnosis Accuracy Test

Checks if the diagnosed root cause matches the simulated fault.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

latency_ms

Time taken to ingest telemetry and emit diagnosis • target: 1500 • range: 0-5000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Implement real-time OPC UA sensor ingestion pipelines with OpenAI Agents SDK

  • Leverage OpenAI o3 reasoning capabilities for root-cause analysis on time-series telemetry

  • Deploy self-healing feedback loops that trigger dynamic PLC register updates

Resources

Dataset notes

Simulated OPC UA time-series data representing motor vibration, bearing temperature, and intake pressure from industrial air compressor units.

Challenge setup

Evaluates the agent's accuracy in identifying telemetry root-causes and executing corrective PLC actions within time thresholds.

Challenge input

JSON containing OPC UA node time-series data

OpenAI Agents SDK

Provides official multi-turn function calling and agent routing for telemetry analysis.

OpenAI o3

Delivers advanced reasoning over complex multi-variable time series.

Evaluated output

JSON with root_cause_id, confidence_score, and recommended_plc_action

Evaluation checks
  • Checks if the diagnosed root cause matches the simulated fault.
  • Time taken to ingest telemetry and emit diagnosis • target: 1500 • range: 0-5000
Configured evaluation evidence
  • Benchmark: Industrial Telemetry Bench 2026
  • Latency Ms target: 1500
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • OpenAI o3 · Optional
Policy Serving
  • o3 · Optional

Evaluation contract

  • Diagnosis Accuracy Test · Weight 1
  • latency_ms · Weight 1

Metrics

  • latency_ms · Target 1500

Benchmarks

  • Industrial Telemetry Bench 2026
  • robotics

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing OPC UA node time-series data

    Output: JSON with root_cause_id, confidence_score, and recommended_plc_action

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Orchestration
  • OpenAI o3 · Reasoning Core

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build OPC UA Remote Telemetry Diagnostics with OpenAI Agents SDK and OpenAI o3