Back to Catalog

Learn PydanticAI By Building A Customer Support Agent

IntermediateGuided Project

Build a customer support chatbot using the PydanticAI framework in this practical, hands-on project. Learn how to create schema-driven agents that can structure, validate, and manage real AI interactions. Designed to sharpen your applied AI skills, this project helps you build reliable, adaptive systems through real-world support scenarios—preparing you to build smarter AI tools and innovate confidently in the field of AI development. PydanticAI has one simple aim to bring that FastAPI feeling to GenAI app development.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • LLM, Generative AI, Python, AI Agent, PydanticAI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • July 9, 2025
About this Guided Project
Regular chatbots often get confused with complicated questions or give unpredictable replies. This can lead to poor customer experiences and missed opportunities to resolve issues effectively. Traditional approaches rely heavily on prompt tuning and still don’t guarantee consistent outputs, making it hard to trust AI in real business scenarios.

With PydanticAI, you can guide the AI to always respond in a clear, organized format — like assigning a ticket category, setting its urgency, and suggesting a helpful reply. By using schema-based validation, the chatbot only returns structured and meaningful data that you define ahead of time. This makes it much easier to process, store, and act on AI responses in your support system.

This project walks you through building a smart customer support chatbot using PydanticAI. You’ll learn how to turn free-text questions into structured responses, prioritize support tickets, and escalate important cases — all within a clean, easy-to-maintain Python setup. It’s perfect for developers who want to bring AI into customer service in a reliable and developer-friendly way.

A Look at the Project Ahead

After completing this lab you will be able to:
  • Understand the core concepts of AI agentic systems and their applications.
  • Learn how to use Pydantic for data modeling, validation, and serialization within agent frameworks.
  • Define and implement modular, structured AI agents using the Pydantic Agentic Framework.
  • Orchestrate multi-step reasoning and decision-making processes in agents.
  • Integrate external tools and APIs to enhance agent capabilities.
  • Design agents that are scalable, interpretable, and maintainable for real-world use cases.

What You'll Need

Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Instructors

Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

Read more

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

Read more

Contributors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

Read more