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.
What you are building
The core problem, expected build, and operating context for this challenge.
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
Evaluates the agent's accuracy in identifying telemetry root-causes and executing corrective PLC actions within time thresholds.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Diagnosis Accuracy Test
Checks if the diagnosed root cause matches the simulated fault.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
latency_ms
Time taken to ingest telemetry and emit diagnosis • target: 1500 • range: 0-5000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
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
- Checks if the diagnosed root cause matches the simulated fault.
- Time taken to ingest telemetry and emit diagnosis • target: 1500 • range: 0-5000
- Benchmark: Industrial Telemetry Bench 2026
- Latency Ms target: 1500
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- OpenAI · Required
- OpenAI o3 · Optional
- 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
- 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
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