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Challenge

Municipal Zoning Variance Predictor with Pydantic AI

Zoning variance applications for high-density developments face intense public scrutiny and delays. Build a type-safe zoning evaluation system using Pydantic AI to parse local zoning variance petitions, enforce structural typing, and predict approval likelihood. Achieve 90%+ classification accuracy across municipal hearing historical records.

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Challenge brief

What you are building

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

Construct a type-safe land-use approval predictor using Pydantic AI agent dependencies and dynamic validation.

Delivery guide

How work is evaluated

Evaluation

Evaluates zoning variance approval prediction accuracy and schema validation strictness.

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

Type Safety Validation

Output strictly conforms to Pydantic schema

Binary check

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

Dimension 2

Prediction Brier Score

Probability accuracy score against historical approval outcomes • target: 0.08 • range: 0-0.25

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

  • Define strict Pydantic schemas for zoning petition inputs and outputs

  • Build Pydantic AI agent pipelines leveraging dependency injection

  • Evaluate proposed variance metrics against municipal master plans

  • Generate automated variance risk assessments with structured error recovery

Resources and assets

Reference links and supporting material

Dataset notes

Historical zoning board variance petitions and formal approval/rejection outcomes.

Challenge setup

Evaluates zoning variance approval prediction accuracy and schema validation strictness.

Challenge input

JSON with requested_height_ft, max_allowed_height_ft, setback_reduction_ft

Pydantic AI

Ensures runtime type safety and structured outputs

Evaluated output

JSON with approval_probability, recommendation

Evaluation checks
  • Output strictly conforms to Pydantic schema
  • Probability accuracy score against historical approval outcomes • target: 0.08 • range: 0-0.25
Configured evaluation evidence
  • Benchmark: ZoningApprovalBench
  • Prediction Brier Score target: 0.08
  • 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
  • Hamming · Optional
  • Yupp AI · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • Type Safety Validation · Weight 1
  • Prediction Brier Score · Weight 1

Metrics

  • Prediction Brier Score · Target 0.08

Benchmarks

  • ZoningApprovalBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with requested_height_ft, max_allowed_height_ft, setback_reduction_ft

    Output: JSON with approval_probability, recommendation

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Municipal Zoning Variance Predictor with Pydantic AI