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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates agent diagnosis accuracy when provided mixed network packet captures and virtual PLC cycle timing logs.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

root_cause_accuracy

Checks if identified component matches true failed hardware/switch.

Binary check

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

Dimension 2

diagnostic_precision

Precision of identified fault categories across 20 synthetic scenario inputs. • target: 0.95 • range: 0.8-1

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

  • 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

Resources and assets

Reference links and supporting material

Dataset notes

Combined virtualized PLC telemetry logs, EtherCAT packet traces, and industrial switch port traffic metrics.

How this agent runs

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 for
  • 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
Proof of success
  • Benchmark: SDC Fault Diagnostics Benchmark
  • Diagnostic Precision target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Claude Code · Required
Observation
  • Claude Code · Required
Policy Serving
  • Claude 4.1 Opus · Optional
  • Claude 4 Sonnet · Optional

Evaluation contract

  • root_cause_accuracy · Weight 1
  • diagnostic_precision · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Software-Defined Control System Anomaly Analyzer with Claude Agents SDK