Claude Agents SDK Situational Awareness Diagnostic with Qwen 3
Network switch failures in process plants can disrupt control logic traffic and blind operators to critical safety alarms. Build an diagnostic agent using Claude Agents SDK integrated with Qwen 3 reasoning models to analyze network failure logs and operator alarm histories. Success is measured by correctly isolating the switch failure root cause within 30 seconds of simulated stream processing.
What you are building
The core problem, expected build, and operating context for this challenge.
Deploy a Claude Agents SDK agent paired with Qwen 3 inference to perform root cause analysis on network switch disruptions impacting safety-critical logic.
How work is evaluated
Evaluates root cause detection and operator guidance accuracy during network switch outages.
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.
correct_root_cause_node
Checks if root cause component is correctly identified.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
rca_precision
Precision of identified root cause and recommended remediation steps. • target: 0.95 • range: 0-1
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 a Claude Agents SDK agent framework setup for diagnostic workflows.
Integrate Qwen 3 model reasoning for complex physical system state analysis.
Correlate network packet drop logs with PLC lost-heartbeat events.
Provide step-by-step situational awareness recovery actions for plant operators.
Reference links and supporting material
Combined network packet capture traces (PCAP), SNMP traps, and SCADA alarm logs during simulated industrial switch outages.
How this agent runs
Evaluates root cause detection and operator guidance accuracy during network switch outages.
Challenge input
JSON object with snmp_traps and scada_alarms arrays
Claude Agents SDK
Required framework for building multi-turn agent workflows.
Qwen 3
Utilized for complex multi-source logical reasoning and fault isolation.
Evaluated output
JSON object with root_cause_component, primary_impact, and recommended_action
- Checks if root cause component is correctly identified.
- Precision of identified root cause and recommended remediation steps. • target: 0.95 • range: 0-1
- Benchmark: OTNetworkRCA-2026
- Rca Precision target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Claude Code · Optional
- Claude Code · Optional
- Qwen 3 · Required
- Claude 4.1 Opus · Optional
Evaluation contract
- correct_root_cause_node · Weight 1
- rca_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.