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.
What you are building
Construct a type-safe land-use approval predictor using Pydantic AI agent dependencies and dynamic validation.
How work is evaluated
Evaluates zoning variance approval prediction accuracy and schema validation strictness.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Type Safety Validation
Output strictly conforms to Pydantic schema
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Prediction Brier Score
Probability accuracy score against historical approval outcomes • target: 0.08 • range: 0-0.25
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
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
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
- Output strictly conforms to Pydantic schema
- Probability accuracy score against historical approval outcomes • target: 0.08 • range: 0-0.25
- Benchmark: ZoningApprovalBench
- Prediction Brier Score target: 0.08
- 1 public reference case
- 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
- Pydantic AI · Required
- Hamming · Optional
- Yupp AI · Optional
- 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
- 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.