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Implement a VASP License Risk Auditor with Pydantic AI & AI SDK

Regulatory compliance officers require fast, type-safe audits of Virtual Asset Service Provider (VASP) license applications against MAS mandates. Develop a Pydantic AI agent with Vercel AI SDK streaming to validate corporate governance and crypto asset custody flows, attaining 100% structured schema compliance.

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What you are building

Build a type-safe VASP compliance verification service using Pydantic AI and Vercel AI SDK to audit digital asset license submissions.

How work is evaluated

Evaluation

Evaluates schema strictness and compliance rule checking against VASP submission documents.

Datasets

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

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

pydantic_schema_valid

Verifies output perfectly adheres to Pydantic schema definitions.

Binary check

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

Dimension 2

audit_accuracy

Accuracy of pass/fail regulatory decisions on synthetic VASP filings. • target: 0.98 • 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

  • Master Pydantic AI for strongly-typed output generation and validation

  • Integrate Vercel AI SDK streaming responses into compliance audit dashboards

  • Implement automated regulatory checks based on MAS VASP licensing guidelines

  • Handle validation errors cleanly using Pydantic's dynamic fallback patterns

Resources

Dataset notes

Synthetic dataset of 50 VASP license applications with varied compliance defects.

Challenge setup

Evaluates schema strictness and compliance rule checking against VASP submission documents.

Challenge input

JSON containing entity details and custody architecture

Pydantic AI

Provides type-safe structured output generation and dependency injection.

Vercel AI SDK

Seamless streaming of agent outputs and UI state updates.

Evaluated output

JSON conforming to strict Pydantic AuditResult model

Evaluation checks
  • Verifies output perfectly adheres to Pydantic schema definitions.
  • Accuracy of pass/fail regulatory decisions on synthetic VASP filings. • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: RegTechComplianceEval
  • Audit Accuracy target: 0.98
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • Pydantic AI · Required
  • Vercel AI SDK · Optional
  • LangWatch · Optional
Orchestration
  • Pydantic AI · Required
  • Vercel AI SDK · Optional

Evaluation contract

  • pydantic_schema_valid · Weight 1
  • audit_accuracy · Weight 1

Metrics

  • audit_accuracy · Target 0.98

Benchmarks

  • RegTechComplianceEval
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing entity details and custody architecture

    Output: JSON conforming to strict Pydantic AuditResult model

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework
  • Vercel AI SDK · Streaming Infrastructure

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Implement a VASP License Risk Auditor with Pydantic AI & AI SDK