Skip to main content
Loading navigation
Back to challenges

Software-Defined Controls (SDC) Testing Agent with Pydantic AI

Software-defined controls require strict type-safe automated testing to prevent runtime PLC crashes. Create a deterministic control software testing harness using Pydantic AI to validate IEC 61131-3 logic against edge simulation models. Ensure 100% type-safe payload validation and zero unhandled exceptions during execution.

EngineeringHosted by Vera
Sign in free to start

What you are building

Build a type-safe test runner agent using Pydantic AI to validate software-defined control logic against physical boundary conditions.

How work is evaluated

Evaluation

Evaluate test generation agent's output against expected type constraints and test coverage criteria.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

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

pydantic_schema_valid

Output passes Pydantic runtime schema validation

Binary check

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

Dimension 2

test_coverage

Coverage of specified control loop edge cases • target: 1 • range: 0-1

Binary check

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

Learning goals

  • Build type-safe control validation models using Pydantic and Pydantic AI

  • Implement test case generation for software-defined control loops (PID, state machines)

  • Enforce rigorous validation on telemetry input/output data ranges

  • Generate automated compliance certificates for edge deployment

Resources

Dataset notes

Control loop parameter specifications and physical limits for SDC testing.

Challenge setup

Evaluate test generation agent's output against expected type constraints and test coverage criteria.

Challenge input

JSON object describing PID loop setpoints and tolerance limits

Pydantic AI

Guarantees strict schema validation for model outputs.

Evaluated output

JSON test report detailing pass/fail results for edge test vectors

Evaluation checks
  • Output passes Pydantic runtime schema validation
  • Coverage of specified control loop edge cases • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: Type-Safe Agent Verification Benchmark
  • Test Coverage target: 1
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • pydantic_schema_valid · Weight 1
  • test_coverage · Weight 1

Metrics

  • test_coverage · Target 1

Benchmarks

  • Type-Safe Agent Verification Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object describing PID loop setpoints and tolerance limits

    Output: JSON test report detailing pass/fail results for edge test vectors

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Type-Safe Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Software-Defined Controls (SDC) Testing Agent with Pydantic AI