Challenge

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.

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Challenge brief

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.

Datasets

Shared data for this challenge

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Evaluation rubric

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.

Max Score: 2
Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1safety_validation_test

Safety Validation Test

Ensure the agent does not suggest a shutdown without checking 'Safety Critical' status.

binary
Weight: 1
Binary check

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

Dimension 2resolution_time

Resolution Time

Time from anomaly detection to work order creation • target: 15 • range: 0-60

binary
Weight: 1
Binary check

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

Learning goals

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.

Start from your terminal
$npx -y @versalist/cli start orchestrating-cross-layer-ot-it-diagnostic-agents-with-openai-agents-sdk-and-qwen-3

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

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Tool Space Recipe

Draft
Action Space
OpenAIOpenAI AI model provider
required
Alibaba Cloud (Qwen)Alibaba's Qwen model series
Policy Serving
Qwen 3
Evaluation
Rubric: 2 dimensions
·Safety Validation Test(1%)
·Resolution Time(1%)
Gold items: 1 (1 public)

Frequently Asked Questions about Orchestrating Cross-Layer OT-IT Diagnostic Agents with OpenAI Agents SDK and Qwen 3