Build AI Agents with GPT-OSS: Open Source AI Workflows

Master agentic AI with 20B GPT-OSS. Build local autonomous agents for weather, location, and professional tasks.

What you’ll learn
✓ Architect autonomous AI agents using the 20 billion parameter GPT-OSS model locally
✓ Implement the core agentic loop: Intent, Planning, Tool Use, and Reflection for business tasks
✓ Integrate real-world APIs for location, weather, and external data retrieval into AI workflows
✓ Develop advanced error handling and conversation memory for robust Enterprise AI solutions

Requirements
● Basic understanding of Python programming
● Hardware capable of running Ollama and large-scale open-source models (20B parameters)
● No paid OpenAI API key required; we focus on local, open-source execution

Description
This course contains the use of artificial intelligence.

In the rapidly evolving landscape of 2025, the ability to build autonomous AI agents is a critical skill for Enterprise professionals. This course, “Build AI Agents with GPT-OSS: Open Source AI Workflows,” provides a comprehensive deep dive into the architecture and deployment of agentic AI. You will learn to move beyond simple chatbots and create systems that can think, plan, and act independently to solve complex tasks.

Using the 20 billion parameter GPT-OSS model, we build a sophisticated “Hiking Agent” that serves as a high-performance blueprint for any business-critical automation. You will learn how to set up a local development environment using Ollama, ensuring total data privacy and significantly reducing dependency on expensive cloud APIs.

The curriculum covers the essential four-step agentic loop: Understanding Intent, Strategic Planning, Tool Execution, and Reflection. We integrate real-world tools like geocoding, weather APIs, and complex data parsing to demonstrate how agents interact with external environments.

Advanced modules focus on Enterprise-grade robustness. You will implement sophisticated error handling to prevent system crashes, add conversation memory for multi-step dialogues, and use reflection techniques to optimize AI decision-making for efficiency. By the end of this course, you will have a fully functional, local AI agent and the expertise to apply these principles to automate complex professional workflows in any industry

Who this course is for
■ Software Engineers and Developers looking to integrate autonomous AI agents into their tech stack
■ Data Professionals seeking local, secure AI alternatives for sensitive Enterprise workflows
■ AI Enthusiasts focused on practical, high-efficiency AI productivity tools