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Challenge

Build an Industrial Complex Financing Agent with Pydantic AI

Structured debt financing for industrial parks like New October ($12m+) requires precise covenant monitoring. Build a type-safe debt monitoring agent using Pydantic AI to parse loan agreements, track drawdown schedules, and enforce debt service coverage ratios.

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Challenge brief

What you are building

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

Utilize Pydantic AI type-safe agent models to analyze bank financing covenants and monitor industrial park drawdown compliance.

Delivery guide

How work is evaluated

Evaluation

Evaluates loan covenant validation accuracy and drawdown approval enforcement.

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

covenant_compliance_check

Ensure tranche is approved only when construction threshold is met

Binary check

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

Dimension 2

covenant_extraction_accuracy

Accuracy of extracted covenant variables • target: 0.99 • range: 0.9-1

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

  • Define strict Pydantic schemas for bank credit facilities and repayment terms

  • Extract financial covenants (LTV, DSCR) from legal loan agreement text

  • Automate drawdown eligibility checks against project construction milestones

  • Output verified covenant compliance certificates for bank credit committees

Resources and assets

Reference links and supporting material

Dataset notes

Industrial development loan agreements, construction drawdown certificates, and ADCB financing covenant disclosures.

Challenge setup

Evaluates loan covenant validation accuracy and drawdown approval enforcement.

Challenge input

JSON milestone and covenant data

Pydantic AI

Type-safe agent framework ideal for strict financial output verification

Evaluated output

JSON drawdown approval

Evaluation checks
  • Ensure tranche is approved only when construction threshold is met
  • Accuracy of extracted covenant variables • target: 0.99 • range: 0.9-1
Configured evaluation evidence
  • Benchmark: FinCovenantBench
  • Covenant Extraction Accuracy target: 0.99
  • 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
  • Coval · Optional
  • Bito AI · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • covenant_compliance_check · Weight 1
  • covenant_extraction_accuracy · Weight 1

Metrics

  • covenant_extraction_accuracy · Target 0.99

Benchmarks

  • FinCovenantBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON milestone and covenant data

    Output: JSON drawdown approval

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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