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In this fourth installment of our CrewAI Mastery series, we dive deep into one of the most powerful features for developers: Structured Outputs. While raw text responses are great for chat, building real-world applications requires data that your code can actually understand. We explore how to transition from standard string outputs to high-precision, validated data using Pydantic and JSON. We’ll walk through the step-by-step implementation of the output_json and output_pydantic parameters within your CrewAI Tasks. You will learn how to define a schema that ensures your AI agents return exactly what you expect every single time—no more parsing messy strings or dealing with unpredictable formatting. This is the "secret sauce" for integrating CrewAI into larger software ecosystems and APIs. By the end of this video, you’ll be able to create complex data structures, enforce type safety, and automate the validation of agent responses. Whether you are building a data extraction pipeline or a sophisticated multi-agent automation, mastering structured output is essential for creating reliable, production-ready AI workflows. Resources: [https://github.com/nithishkumar86/CrewAI_C...]