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Legal Reviewer Agent with Claude Sonnet 4

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Linked challenge: Structured M&A Due Diligence with Pydantic AI, GPT-5, and Claude Sonnet 4

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Linked challenge

Structured M&A Due Diligence with Pydantic AI, GPT-5, and Claude Sonnet 4

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Create a 'LegalReviewerAgent' using Pydantic AI. This agent should take a 'LegalDocumentSummary' Pydantic model as input and output a 'LegalReview' Pydantic model, including identified liabilities and IP status. Show how this agent interacts with Claude Sonnet 4 via Amazon Bedrock to analyze legal text and how Pydantic validates the structured output. Emphasize error handling for invalid outputs.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

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Hold the task contract and output shape stable so generated implementations remain comparable.

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Update libraries, interfaces, and environment assumptions to match the stack you actually run.

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Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Structured M&A Due Diligence with Pydantic AI, GPT-5, and Claude Sonnet 4

This challenge focuses on building a robust, type-safe multi-agent system using Pydantic AI for conducting financial and legal due diligence in mergers and acquisitions. Developers will design agents that leverage the distinct strengths of GPT-5 for complex financial modeling and market analysis, and Claude Sonnet 4 for detailed legal document review and risk identification. The critical aspect of this system is Pydantic AI, which ensures strict output validation and type safety across all agent interactions and generated reports, vital for accuracy in financial and legal contexts. The solution requires integrating these advanced models via scalable inference platforms like Amazon Bedrock and deploying the agent system efficiently using Modal. Agents will be designed to interact with simulated enterprise data sources, perform structured analysis, identify red flags, and generate a comprehensive, validated M&A due diligence report. This project emphasizes the importance of structured outputs and data integrity in critical business applications of generative AI.

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