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

本课程是 Build Powerful AI Agents with OpenAI Tools 专业证书的一部分

位教师：Professionals from the Industry

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4个模块

深入了解一个主题并学习基础知识。

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2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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您将学习的工具

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积累 Software Development 领域的专业知识

本课程是 Build Powerful AI Agents with OpenAI Tools 专业证书专项课程的一部分

在注册此课程时，您还会同时注册此专业证书。

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该课程共有4个模块

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.

单元详情

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.

涵盖的内容

2个视频7篇阅读材料3个作业

2个视频总计12分钟

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

7篇阅读材料总计145分钟

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

3个作业总计120分钟

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

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.

涵盖的内容

3个视频3篇阅读材料2个作业2个非评分实验室

3个视频总计23分钟

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

3篇阅读材料总计85分钟

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

2个作业总计90分钟

Module 2 Assessment60分钟
API Setup and Management30分钟

2个非评分实验室总计50分钟

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

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.

涵盖的内容

3个视频4篇阅读材料3个作业3个非评分实验室

3个视频总计17分钟

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

4篇阅读材料总计95分钟

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

3个作业总计120分钟

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

3个非评分实验室总计70分钟

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

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.