Microsoft Agent Framework Tutorial | Agent with Structured Output (Part-4)

Welcome to Part 4 of my Microsoft Agent Framework series! 📦 Resources & Links: 🔗 Code: https://github.com/Sandesh-hase/Microsoft-Agent-Framework.git End to End Project: https://youtu.be/BAdcNKf5d2g Part1: https://youtu.be/Gxh6fef4jJU Part2: https://youtu.be/i_wfwkgaFpI Part3: https://youtu.be/FfUYmu0akbI In this episode, we dive deep into one of the most powerful features of MAF — Structured Output. Instead of returning long conversational responses, the agent can now generate clean, predictable, machine-readable JSON using Pydantic models. This is essential when you're building production-grade workflows where accuracy and data consistency truly matter. To make this practical, we take a real-world use case: parsing a resume PDF. Using PyMuPDF (fitz), we extract text directly from a resume and pass it to the agent with a structured schema. The agent then returns perfectly formatted data such as name, email, phone, skills, total experience, education, and last job title — all mapped into a proper Pydantic model. This is the same technique used in modern ATS, HRMS, and enterprise data pipelines. By the end of the video, you'll understand: ✔ How to define structured output schemas ✔ How to enforce JSON-safe output using Pydantic ✔ How to parse PDFs in Python ✔ How to build a simple AI Resume Parser ✔ Why structured output is crucial for real enterprise applications If you're following the Microsoft Agent Framework series, make sure to watch Parts 1–3 as well! More exciting chapters coming soon. #MicrosoftAgentFramework #StructuredOutput #AzureOpenAI #AIResumeParser #PythonAI #PydanticModels #AzureAI #AgentFramework #AIEngineering #ResumeParsing #PDFExtraction #PyMuPDF #LLMApplications