Software-Defined Control System Anomaly Analyzer with Claude Agents SDK
Software-defined control (SDC) decouples control logic from underlying hardware, increasing exposure to network latency and switch failures. Construct an intelligent log diagnostic agent using Claude Agents SDK and Shakudo to identify control loop timing deviations and pinpoint network switch anomalies.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an SDC log diagnostic workflow leveraging Claude Agents SDK deployed on Shakudo to isolate digital network failures affecting PLC control loops.
How work is evaluated
Evaluates agent diagnosis accuracy when provided mixed network packet captures and virtual PLC cycle timing logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
root_cause_accuracy
Checks if identified component matches true failed hardware/switch.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
diagnostic_precision
Precision of identified fault categories across 20 synthetic scenario inputs. • target: 0.95 • range: 0.8-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Configure Claude Agents SDK tools for multi-file SDC diagnostic parsing
Deploy model execution pipelines using the Shakudo platform runtime
Analyze network switch latency impact on virtual PLC cycle time stability
Generate actionable root-cause failure reports for automation engineers
Reference links and supporting material
Combined virtualized PLC telemetry logs, EtherCAT packet traces, and industrial switch port traffic metrics.
Challenge setup
Evaluates agent diagnosis accuracy when provided mixed network packet captures and virtual PLC cycle timing logs.
Challenge input
JSON containing network_switch_logs and virtual_plc_cycle_times.
Claude Agents SDK
Provides sophisticated extended reasoning for complex log debugging.
Shakudo
Manages execution environment, agent scaling, and data integrations.
Evaluated output
JSON containing faulty_component, anomaly_type, and confidence_score.
- Checks if identified component matches true failed hardware/switch.
- Precision of identified fault categories across 20 synthetic scenario inputs. • target: 0.95 • range: 0.8-1
- Benchmark: SDC Fault Diagnostics Benchmark
- Diagnostic Precision target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Claude Code · Required
- Claude Code · Required
- Claude 4.1 Opus · Optional
- Claude 4 Sonnet · Optional
Evaluation contract
- root_cause_accuracy · Weight 1
- diagnostic_precision · Weight 1
Metrics
- diagnostic_precision · Target 0.95
Benchmarks
- SDC Fault Diagnostics Benchmark
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing network_switch_logs and virtual_plc_cycle_times.
Output: JSON containing faulty_component, anomaly_type, and confidence_score.
Execution harness: Python
Evaluation tools
- Claude Agents SDK · Agentic Reasoning
- Shakudo · Agent Infrastructure Platform
Recipe freeze marker
No recipe freeze time is recorded.