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Challenge

Deploy Type-Safe Multi-Agent Orchestration with Pydantic AI and Claude Sonnet 4.6.6

Create a robust agent team for corporate policy analysis and regulatory compliance monitoring, inspired by SAP's recent antitrust regulatory pivot. This challenge focuses on building type-safe agent workflows where every action and response is validated through Pydantic models. You will coordinate between specialized agents using Mastra AI to ensure compliant and structured communication between system components.

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Challenge brief

What you are building

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

Create a robust agent team for corporate policy analysis and regulatory compliance monitoring, inspired by SAP's recent antitrust regulatory pivot. This challenge focuses on building type-safe agent workflows where every action and response is validated through Pydantic models. You will coordinate between specialized agents using Mastra AI to ensure compliant and structured communication between system components.

Datasets

Shared data for this challenge

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

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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

SchemaValidation

Ensure output matches pydantic model

Binary check

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

Dimension 2

ComplianceRate

Percentage of successful validations • 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

  • Master Pydantic AI dependency injection and schema enforcement for LLM outputs

  • Orchestrate complex agent team workflows using the Mastra AI framework

  • Implement tracing and evaluation loops using Arize Phoenix for observability

  • Design structured communication protocols between specialized agents

  • Leverage Cursor for AI-assisted development of compliance checking modules

  • Utilize Bito AI for rapid generation of edge-case scenarios to test agent policy adherence

How this agent runs

Evaluate agent compliance with policy definitions.

Preview configuration

Challenge input

Policy text

Pydantic AI

Typed Python agent framework.

Mastra AI

TypeScript agent framework.

Bito AI

AI assistant for dev tasks

Evaluated output

Validated JSON

Checks for
  • Ensure output matches pydantic model
  • Percentage of successful validations • target: 1 • range: 0-1
Proof of success
  • ComplianceRate target: 100%
  • 1 public reference case
Runtime evidence
  • Python execution harness
View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Mastra AI · Optional
  • Bito AI · Optional
Orchestration
  • Pydantic AI · Required
  • Mastra AI · Optional

Evaluation contract

  • SchemaValidation · Weight 1
  • ComplianceRate · Weight 1

Recipe state

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

Run this agent on your dataset

Versalist can run this agent on your behalf with your data. Tell us about your dataset and the result you need.

Discuss your dataset
Start from your terminal
$npx -y @versalist/cli start deploy-type-safe-multi-agent-orchestration-with-pydantic-ai-and-claude-sonnet-4-6-6

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

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