What is an AI agent in this course?

In this course, an AI agent is a system that can reason through a request, use tools, keep track of context, and take action across multiple steps. The focus is on assembling those parts into a reliable OpenAI-based workflow rather than treating the model like a one-turn assistant.

When would you use an AI agent?

You would use an AI agent when a task involves multiple steps, outside information, or a choice about what action to take next. In this course, that means work that needs document lookup, web access, calculations, or other tool-assisted steps to complete the request.

How does an AI agent fit into a broader workflow?

It sits between the user's request and the final result, coordinating the steps needed to gather information, make decisions, and return a response. The course treats agent building as a connected workflow that combines reasoning, memory, and tool use instead of isolated prompt-and-answer exchanges.

How is an AI agent different from a reactive chatbot?

A reactive chatbot mainly replies to prompts with fixed or limited response patterns, while an AI agent is designed to plan, use tools, and work through a problem in stages. This course emphasizes that difference by focusing on action-taking and multi-step problem solving, not just conversation.

Do you need any prerequisites before learning to build an AI agent?

A basic understanding of APIs and coding workflows is helpful because the course works with request handling, tool integration, and conversation state. Since the course is intermediate, it helps to be comfortable reading and modifying technical examples as you build and test the agent.

What tools, platforms, or methods are used in this course?

The course centers on OpenAI's Responses API and built-in tools such as browser, code interpreter, and file search. It also uses system prompts and reasoning patterns to shape agent behavior and improve reliability.

What specific tasks will you practice or complete in this course?

You practice designing the agent architecture, configuring API access, managing conversation state, and connecting tools to user requests. You also test multi-step behavior, add fallback and caching logic, and refine prompts so the agent responds more reliably.

Building Your First AI Agent with OpenAI

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Building Your First AI Agent with OpenAI

This course is part of Build Powerful AI Agents with OpenAI Tools Professional Certificate

Instructor: Professionals from the Industry

3,290 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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This course is part of the Build Powerful AI Agents with OpenAI Tools Professional Certificate

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Coursera

There are 4 modules in this course

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.

Module details

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.

What's included

2 videos7 readings3 assignments

2 videosTotal 12 minutes

The Five Core Components That Make an Agent8 minutes
The OpenAI Agent Ecosystem in Action4 minutes

7 readingsTotal 145 minutes

Why Your Business Needs Agents, Not Just Chatbots20 minutes
Agent Architecture Patterns and Decision Framework10 minutes
Hands on Activity: Design Your Agent Architecture30 minutes
Mapping OpenAI Tools to Agent Architecture20 minutes
Activity: Build Your OpenAI Stack Blueprint25 minutes
Tool Design Patterns and Best Practices15 minutes
Activity: Design Your Tool Strategy25 minutes

3 assignmentsTotal 120 minutes

Agents vs. Chatbots Fundamentals30 minutes
OpenAI Agent Stack Mastery30 minutes
Module 1 Assessment60 minutes

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.

What's included

3 videos3 readings2 assignments2 ungraded labs

3 videosTotal 23 minutes

Your First Steps with the OpenAI API8 minutes
Building a Production-Ready API Client7 minutes
From API to Agent: Your First Intelligent System7 minutes

3 readingsTotal 85 minutes

API Best Practices and Cost Optimization45 minutes
Agent Design with System Prompts20 minutes
Reasoning Patterns for AI Agents20 minutes

2 assignmentsTotal 90 minutes

API Setup and Management30 minutes
Module 2 Assessment60 minutes

2 ungraded labsTotal 50 minutes

Build Your API Client Foundation25 minutes
Create Your Q&A Agent Foundation25 minutes

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.

What's included

3 videos4 readings3 assignments3 ungraded labs

3 videosTotal 17 minutes

Configuring and Optimizing Browser Tool Usage8 minutes
Implementing Document Search and Retrieval3 minutes
Adding Analytical Power to Your Agent5 minutes

4 readingsTotal 95 minutes

Your Agent and How it Interacts with OpenAI Tools10 minutes
Search Strategy Design and Information Synthesis45 minutes
Document Processing and Retrieval Patterns20 minutes
Code Interpreter for Data Analysis20 minutes

3 assignmentsTotal 120 minutes

Browser Tool Mastery30 minutes
File Search Fundamentals30 minutes
Module 3 Assessment60 minutes

3 ungraded labsTotal 70 minutes

Build Search-Enabled Support Agent25 minutes
Build a Policy Documentation Assistant25 minutes
Create Analytical Agent20 minutes

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.