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Create an Agentic Code Assistant with GPT-5

IntermediateGuided Project

You probably use Cursor, why not learn how to build your own? Using LangChain and OpenAI's inexpensive GPT-5 mini model, you'll create an agentic code assistant that writes, debugs, and explains code. Step by step, this project will connect the pieces that make agents work—prompts, tools, and reasoning. By the end, you’ll be ready to apply what you've learned and start building agentic systems of your own.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Artificial Intelligence, LangChain, AI Agent, GPT-5, Generative AI, Agentic AI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 90 minutes

Platform

  • SkillsNetwork

Last Update

  • August 27, 2025
About this Guided Project
With the rise of AI-assisted coding tools and frameworks, developers now have the opportunity to go beyond autocomplete and build true agentic coding assistants that can reason, act, and collaborate on code. This guided project explores how to use LangChain and OpenAI’s GPT-5 mini model to create a code assistant that doesn’t just suggest snippets, but actively writes, debugs, and explains code through structured reasoning and tool use. Instead of static helpers that only complete text, you’ll construct a dynamic AI system that reasons step by step, calls external tools, and adapts its approach—giving you hands-on experience with the foundations of agentic AI.

What You'll Learn

By the end of this project, you will be able to:
  • Build an agentic coding assistant with LangChain + GPT-5: Learn how to design an AI that generates, debugs, and explains code while reasoning about problems systematically.
  • Design and integrate custom tools in LangChain: Extend your assistant’s capabilities by creating Python tools for code analysis, debugging, or other domain-specific tasks.
  • Implement prompt engineering and reasoning strategies: Understand how to structure prompts that guide GPT-5’s reasoning, making your assistant more reliable and effective.

Who Should Enroll

  • Early-career AI/ML engineers looking to expand beyond model usage into building agentic systems that can reason and act in real-world coding tasks.
  • Software developers who use tools like GitHub Copilot and want to understand how such assistants are built from scratch with LangChain and GPT models.
  • Technical product managers aiming to gain practical insight into agentic AI workflows, giving them the knowledge to design and scope intelligent developer tools.

Why Enroll

This project bridges the gap between AI theory and practical agent building, giving you the skills to create assistants that code with you instead of for you. You’ll learn how to integrate GPT-5 into LangChain, design custom tools, and manage multi-step reasoning workflows—skills that are central to the next generation of AI-powered software development. By the end, you won’t just understand agentic AI in theory—you’ll have a fully functioning code assistant you built yourself, plus the confidence to expand it into more advanced agents.

What You'll Need

To get the most out of this project, you should have a working knowledge of Python programming and some familiarity with language models or APIs. Prior experience with LangChain is helpful but not required. All dependencies are pre-configured in the environment, and the project runs best on current versions of Chrome, Edge, Firefox, or Safari.

Instructors

Tenzin Migmar

Data Scientist

Hi, I'm Tenzin. I'm a data scientist intern at IBM interested in applying machine learning to solve difficult problems. Prior to joining IBM, I worked as a research assistant on projects exploring perspectivism and personalization within large language models. In my free time, I enjoy recreational programming and learning to cook new recipes.

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Contributors

Karan Goswami

Data Scientist at IBM

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.

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