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
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.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
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.
Learning goals
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%
Resources
Simulated 24-hour time-series telemetry dataset of a wastewater treatment plant's aeration basin equipped with small PLC controllers.
Challenge setup
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 (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
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
Metrics
- anomaly_precision · Target 0.92
- latency_ms · Target 300
Benchmarks
- Industrial Telemetry Benchmark v1
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON lines with timestamp, dissolved_oxygen, air_flow_rate, blower_rpm, valve_position
Output: JSON object with anomaly_detected (bool), root_cause (string), confidence (float), latency_ms (int)
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
- Qwen 3 · Language Model
Recipe freeze marker
No recipe freeze time is recorded.