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Challenge

Asset Management Fund Tax Incentive Validator with Pydantic AI & GPT-5 Pro

Singapore's asset management sector requires automated tax incentive eligibility calculations under updated MAS schemes. Build a type-safe qualification workflow using Pydantic AI and GPT-5 Pro to parse fund structures and output validated tax scheme recommendations.

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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 asset management tax eligibility agent using Pydantic AI and GPT-5 Pro reasoning models.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of structured fund eligibility output against MAS tax rules.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

strict_pydantic_validation

Ensures schema strictness

Binary check

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

Dimension 2

eligibility_accuracy

Correct classification of eligibility status • 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

  • Implement structured dynamic models using Pydantic AI

  • Leverage GPT-5 Pro for complex legal taxonomy reasoning

  • Validate fund spend and local employment minimum threshold requirements

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic asset fund profiles with varying AUM, staffing, and local expenditure figures.

How this agent runs

Evaluates accuracy of structured fund eligibility output against MAS tax rules.

Challenge input

JSON containing AUM, local spend, local professionals count

Pydantic AI

Type safety and schema validation

GPT-5 Pro

Complex regulatory reasoning

Evaluated output

JSON containing eligible_scheme, compliant, shortfall_details

Checks for
  • Ensures schema strictness
  • Correct classification of eligibility status • target: 1 • range: 0-1
Proof of success
  • Benchmark: TaxEval-Bench
  • Eligibility Accuracy target: 1
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • GPT-5 Pro · Optional
  • Cekura · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • strict_pydantic_validation · Weight 1
  • eligibility_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Asset Management Fund Tax Incentive Validator with Pydantic AI & GPT-5 Pro