Orchestrating Cross-Layer OT-IT Diagnostic Agents with OpenAI Agents SDK and Qwen 3
Industrial environments suffer from a data silo problem between the factory floor (Operational Technology) and business systems (Information Technology). This challenge requires building a multi-agent system using the OpenAI Agents SDK to bridge the Purdue Model layers (Levels 0-4). You will implement a 'Bridge Agent' that monitors real-time SCADA telemetry and correlates it with IT-side maintenance logs and spare-parts inventory. The system must use OpenAI's handoff mechanism to move tasks between a 'Telemetry Specialist' (running Qwen 3 for high-throughput sensor analysis) and a 'Maintenance Strategist' (running GPT-4o for strategic planning). Your solution will address the 'New IT and OT don't always mix' problem by creating a unified command layer. The agents must be capable of identifying a failing gas stabilizer based on pressure fluctuations (Source: Emerson Movicon) and automatically querying an ERP database for available replacement valves. The final output must be a root-cause analysis report and a pre-drafted work order, ensuring that digital communications do not compromise safety-critical logic by implementing strict verification steps.
What you are building
The core problem, expected build, and operating context for this challenge.
Industrial environments suffer from a data silo problem between the factory floor (Operational Technology) and business systems (Information Technology). This challenge requires building a multi-agent system using the OpenAI Agents SDK to bridge the Purdue Model layers (Levels 0-4). You will implement a 'Bridge Agent' that monitors real-time SCADA telemetry and correlates it with IT-side maintenance logs and spare-parts inventory. The system must use OpenAI's handoff mechanism to move tasks between a 'Telemetry Specialist' (running Qwen 3 for high-throughput sensor analysis) and a 'Maintenance Strategist' (running GPT-4o for strategic planning). Your solution will address the 'New IT and OT don't always mix' problem by creating a unified command layer. The agents must be capable of identifying a failing gas stabilizer based on pressure fluctuations (Source: Emerson Movicon) and automatically querying an ERP database for available replacement valves. The final output must be a root-cause analysis report and a pre-drafted work order, ensuring that digital communications do not compromise safety-critical logic by implementing strict verification steps.
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, how much each dimension matters, and which criteria separate a passable run from a strong one.
Safety Validation Test
Ensure the agent does not suggest a shutdown without checking 'Safety Critical' status.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Resolution Time
Time from anomaly detection to work order creation • target: 15 • range: 0-60
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the 'Agent' and 'Runner' classes in the OpenAI Agents SDK to manage stateful industrial conversations.
Implement Tool Calling to interface with a simulated Level 2 SCADA system (OPC UA data streams).
Design a semantic routing layer that determines when to escalate from Qwen 3 local analysis to GPT-4o strategic reasoning.
Build a 'Safety Interlock' agent that validates every maintenance suggestion against P&ID (Piping and Instrumentation Diagram) constraints.
Orchestrate a multi-turn workflow that connects transient sensor anomalies to long-term asset management goals.
Optimize data serialization between OT binary formats and LLM-friendly JSON contexts.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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