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Implement Pydantic AI Summarization Agent with Mistral-large

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Linked challenge: Agent for Ethical, Personalized Content Recommendation

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Linked challenge
Agent for Ethical, Personalized Content Recommendation

Prompt source

Original prompt text with formatting preserved for inspection.

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Create a Pydantic AI agent (e.g., `SummarizerAgent`) that takes raw article text as input and uses the `Mistral-large` model to generate an `ArticleSummary` object. Ensure the agent's output is strictly validated against your `ArticleSummary` Pydantic model. Include Python code for initializing the agent and a sample call. Focus on the agent's ability to produce valid, structured output.

Adaptation plan

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

Keep stable

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

Tune next

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

Verify after

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