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Challenge

Type-Safe SCADA Alarm Triaging with Pydantic AI

Factory operators suffer from alarm fatigue when thousands of unprioritized SCADA alarms trigger during line downtime events. Build a type-safe alarm triaging agent using Pydantic AI that ingests raw telemetry alarm vectors and produces strict Pydantic schema validated alert action plans. Achieve 100% schema validation compliance with sub-100ms structured inference.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a type-safe alarm parsing and triaging agent using Pydantic AI for structured SCADA root cause diagnostic responses.

Delivery guide

How work is evaluated

Evaluation

Evaluates schema compliance, validation error rates, and alarm grouping accuracy under heavy alarm load.

Datasets

Challenge datasets

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

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

schema_validation_pass

Ensure zero schema validation errors in response model

Binary check

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

Dimension 2

root_cause_accuracy

Accuracy in correctly identifying primary cascading root cause alarm • target: 0.95 • 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

What you should walk away with

  • Build a Pydantic AI Agent leveraging strict Pydantic model response validation

  • Parse high-density SCADA alarm bursts into prioritized root cause action hierarchies

  • Ensure robust error recovery and fallback schema handling when inputs contain missing data

Resources and assets

Reference links and supporting material

Dataset notes

SCADA alarm logs captured during simulated multi-equipment trip events across chemical process lines.

Challenge setup

Evaluates schema compliance, validation error rates, and alarm grouping accuracy under heavy alarm load.

Challenge input

JSON array of raw SCADA alarm records

Pydantic AI

Provides type-safe agent execution with native Pydantic validation guarantees.

Evaluated output

Pydantic JSON object conforming to TriageResult schema

Evaluation checks
  • Ensure zero schema validation errors in response model
  • Accuracy in correctly identifying primary cascading root cause alarm • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: ISA-18.2 Alarm Triage Suite (ALARM-TRIAGE-2025)
  • Root Cause Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • 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

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

Evaluation contract

  • schema_validation_pass · Weight 1
  • root_cause_accuracy · Weight 1

Metrics

  • root_cause_accuracy · Target 0.95

Benchmarks

  • ISA-18.2 Alarm Triage Suite (ALARM-TRIAGE-2025)
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of raw SCADA alarm records

    Output: Pydantic JSON object conforming to TriageResult schema

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Type-Safe SCADA Alarm Triaging with Pydantic AI