OpenAI Agents SDK PLC Telemetry Anomaly Detector with Qwen 3
Wastewater aeration retrofits require reliable telemetry monitoring to prevent dissolved oxygen control failures. Build an agentic monitoring agent using the OpenAI Agents SDK and Qwen 3 to detect telemetry anomalies across small PLC retrofits. The solution must reduce false alarms by 35% while flagging real aeration faults within 2 seconds.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an autonomous PLC telemetry anomaly detector using OpenAI Agents SDK tool calls and Qwen 3 model execution.
How work is evaluated
Evaluates agent anomaly detection precision, latency, and function calling validity against simulated PLC telemetry logs.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
schema_conformance
Verifies output structure matches exact telemetry evaluation output schema
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
anomaly_precision
Precision score of anomaly classification • target: 0.92 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
latency_ms
Average response latency per packet in milliseconds • target: 300 • range: 0-2000
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 PLC telemetry stream ingestion with OpenAI Agents SDK
Configure Qwen 3 function calling to identify dissolved oxygen anomalies
Develop root-cause analysis logic for wastewater aeration blower controls
Validate automated alert triage to reduce false positive rates below 5%
Reference links and supporting material
Simulated 24-hour time-series telemetry dataset of a wastewater treatment plant's aeration basin equipped with small PLC controllers.
How this agent runs
Evaluates agent anomaly detection precision, latency, and function calling validity against simulated PLC telemetry logs.
Challenge input
JSON lines with timestamp, dissolved_oxygen, air_flow_rate, blower_rpm, valve_position
OpenAI Agents SDK
Required framework for building autonomous function-calling agents.
Qwen 3
Open-weights model assigned for domain reasoning and function calling.
Evaluated output
JSON object with anomaly_detected (bool), root_cause (string), confidence (float), latency_ms (int)
- Verifies output structure matches exact telemetry evaluation output schema
- Precision score of anomaly classification • target: 0.92 • range: 0-1
- Average response latency per packet in milliseconds • target: 300 • range: 0-2000
- Benchmark: Industrial Telemetry Benchmark v1
- Anomaly Precision target: 0.92
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- OpenAI · Required
- Alibaba Cloud (Qwen) · Optional
- Qwen 3 · Optional
Evaluation contract
- schema_conformance · Weight 1
- anomaly_precision · Weight 1
- latency_ms · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.