Building Your First AI Agent with OpenAI

Overview

The Building Your First AI Agent with OpenAI course provides a practical introduction to creating intelligent, tool-using AI agents. Learners begin by understanding the key differences between reactive chatbots and proactive agents, mapping out the five core components of agentic architecture. The course then explores the OpenAI Responses API, GPT-4/5 models, and built-in tools such as browser, code interpreter, and file search, showing how they extend agent capabilities for real-world tasks. Through guided lessons, learners configure secure API access, manage tokens and costs, and design system prompts that define agent behavior. They also add reasoning patterns like chain-of-thought and reflection to improve reliability, before integrating multiple tools into a unified agent system. Hands-on projects, including building a technical support agent, reinforce skills in architecture design, tool integration, and performance optimization. By the end, learners will have built a fully functioning AI agent capable of handling complex multi-step tasks and decision-making with autonomy and reliability.

Syllabus

Understanding Agentic AI Architecture

As a new Associate Consultant with the AI consultancy Praxis AI, you will address the limitations of Innovate Logistics' failing customer support chatbot. Your goal is to apply the ACTOR framework to diagnose why the current system fails and design a comprehensive architectural blueprint for a new autonomous agent, utilizing the five core components of intelligent AI systems.

Responses API Fundamentals

Stepping into the role of Prototype Developer at Praxis AI, you will write the code that powers the Innovate Logistics agent's core existence. You will build a production-grade API client that prioritizes security, cost management, and reliability, and then construct a conversational agent capable of maintaining context and following complex behavioral instructions via system prompts.

Working with Built-in Tools

Acting as an Integration Specialist for Praxis AI, you will give the Innovate Logistic agent the tools it needs to solve real business problems. You will implement the Web Search tool for real-time shipping data, deploy File Search (RAG) to access internal policy documents, and utilize the Code Interpreter to build a separate internal agent that analyzes performance logs for management.

Agent Integration Project

In the role of Lead Architect at Praxis AI, you will refactor the disparate tools of the Innovate Logistics Agent into a single, unified intelligent system. You will write the decision-making logic that orchestrates tool usage, implements cost-saving caching and resilient fallback patterns, and validates the final agent through a rigorous mock-and-live testing strategy.