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Challenge

Build a Type-Safe Energy System Telemetry Agent with Pydantic AI

Integrating green hydrogen production and power system telemetry requires end-to-end type safety and validated structured outputs. Build a production-grade agent with Pydantic AI and Pydantic validation to stream clean operational metrics from Salalah energy systems with zero runtime type validation errors.

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Challenge brief

What you are building

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

Develop a strictly typed telemetry processing agent using Pydantic AI framework and Pydantic data schemas.

Delivery guide

How work is evaluated

Evaluation

Evaluates Pydantic AI schema validation and dependency injection on Salalah IoT sensor data.

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_test

Verifies that output passes Pydantic BaseModel validation

Binary check

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

Dimension 2

type_safety_compliance

Percentage of valid responses adhering to exact Pydantic schema • 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

What you should walk away with

  • Build type-safe agent models using Pydantic AI Agent class

  • Enforce dependency injection for live IoT database connectors

  • Validate complex telemetry payloads (electrolyzer efficiency, grid frequency) with Pydantic BaseModel

  • Guarantee structured JSON outputs without post-hoc regex parsing

Resources and assets

Reference links and supporting material

Dataset notes

Telemetry logs from Salalah AI and hydrogen energy demonstration systems including electrolyzer cell health.

Challenge setup

Evaluates Pydantic AI schema validation and dependency injection on Salalah IoT sensor data.

Challenge input

JSON raw telemetry payload with temperature, pressure, current

Pydantic AI

Type-safe agent execution with Pydantic

Evaluated output

Validated Pydantic model instance as JSON containing status, safety_override_active

Evaluation checks
  • Verifies that output passes Pydantic BaseModel validation
  • Percentage of valid responses adhering to exact Pydantic schema • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: Structured Output Evaluation Bench
  • Type Safety Compliance target: 1
  • 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
  • Distyl · Optional
  • All Hands AI · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • schema_validation_test · Weight 1
  • type_safety_compliance · Weight 1

Metrics

  • type_safety_compliance · Target 1

Benchmarks

  • Structured Output Evaluation Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON raw telemetry payload with temperature, pressure, current

    Output: Validated Pydantic model instance as JSON containing status, safety_override_active

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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