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Advanced Tool Development and Integration

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

Instructor: Professionals from the Industry

Included with

Learn more

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

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your Software Development expertise

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

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
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 Advanced Tool Development and Integration course builds on foundational agent skills by focusing on how to create, customize, and integrate tools into intelligent agents. Learners begin by designing custom functions and APIs that extend agent capabilities beyond built-in options, using best practices for clarity, reliability, and safety.

The course then covers connecting agents to third-party services through secure authentication (including OAuth), enabling them to interact with external platforms such as CRMs, databases, and SaaS applications. Emphasis is placed on tool ecosystems, including Modular Component Protocol (MCP) standards for scalable interoperability. Learners also explore persistent memory and state management techniques, allowing agents to maintain continuity across sessions and tasks. Through guided coding activities, dialogues, and a capstone project, participants will design and deploy a Custom Analytics Agent that integrates multiple data sources, performs real-time analysis, and delivers actionable insights. By course end, learners will be able to engineer tools that empower agents with advanced functionality and seamless integrations.

Module details

You are an AI consultant building an agent for Innovate Logistics, a company struggling with a "naive" AI agent that cannot answer specific business questions like calculating shipping costs. Your mission is to fix this by building Agent-Ready Functions—proprietary tools that bridge the gap between the LLM and the company's internal logic. In this module, you will learn to create the "Function Contract" by writing detailed JSON specifications for the AI and robust, validated Python implementations that fulfill them.

What's included

3 videos2 readings3 assignments3 ungraded labs

3 videosTotal 16 minutes

1.1 Creating Your First Custom Tool7 minutes
1.5 From 'Working' to 'Production-Ready'4 minutes
1.9 Integrating Your Toolkit with an Agent5 minutes

2 readingsTotal 20 minutes

1.2 The Agent-Ready Function Contract10 minutes
1.6 Enterprise Patterns for Agent Functions10 minutes

3 assignmentsTotal 120 minutes

1.4 Custom Functions Fundamentals30 minutes
1.8 Production and Testing Patterns30 minutes
1.12 GRADED QUIZ60 minutes

3 ungraded labsTotal 100 minutes

1.3 Refactor the Legacy Logistics Functions35 minutes
1.7 Harden and Test the Logistics Toolkit35 minutes
1.10 Integrate and Test the Logistics Agent30 minutes

You are an AI consultant for Praxis AI, beginning a new project for the client FinCorp. Your task is to build an executive-level Financial Analysis Agent. The challenge shifts from simply building a single reliable tool to architecting an agent capable of autonomously choosing from and sequencing multiple custom tools to handle complex financial analysis queries.

What's included

3 videos3 readings3 assignments3 ungraded labs

3 videosTotal 8 minutes

2.1 Intelligent Function Calling3 minutes
2.5 Wrapping External APIs as Agent Tools2 minutes
2.10 Debugging Complex Tool Interactions3 minutes

3 readingsTotal 30 minutes

2.2 Function Calling Architecture and Patterns10 minutes
2.6 API Integration Best Practices10 minutes
2.11 Comprehensive Testing Strategies for Tool-Using Agents10 minutes

3 assignmentsTotal 120 minutes

2.4 Function Calling Mastery30 minutes
2.9 API Integration Mastery30 minutes
2.13 Module 2 Assessment60 minutes

3 ungraded labsTotal 100 minutes

2.3 Implement the Orchestration Logic35 minutes
2.7 Build and Integrate the API Wrapper35 minutes
2.12 Validate the FinCorp Multi-Tool Agent30 minutes

You are continuing in your consulting role, this time working with Execu-Pal, a startup with an AI executive assistant prototype that currently relies on insecure static API keys. Your mission is to re-architect the system to securely access user-specific data, such as private emails and Slack messages, by implementing OAuth 2.0 Authorization. To ensure the platform is future-proof and interoperable, you will also standardize the entire tool suite using the Model Context Protocol (MCP), enabling the agent to work seamlessly across different AI models.

What's included

2 videos2 readings2 assignments2 ungraded labs

2 videosTotal 6 minutes

3.1 Implementing Secure OAuth Agents4 minutes
3.5 Write Once, Use Everywhere with Model Context Protocol (MCP)3 minutes

2 readingsTotal 22 minutes

3.2 OAuth 2.0 and Service Authentication Patterns12 minutes
3.6 Understanding the Multi-Cloud Platform (MCP) Architecture10 minutes

2 assignmentsTotal 90 minutes

3.4 OAuth and Authentication30 minutes
3.9 Module 3 Assessment60 minutes

2 ungraded labsTotal 60 minutes

3.3 Connect Your Agent to Slack40 minutes
3.7 Migrating Tools to a Local MCP Server20 minutes

You return as an AI consultant working with Execu-Pal for Phase 2 of the engagement. While the agent now has tools, users are complaining that it is "forgetful" (asking for meeting preferences every time) and fragile (crashing when APIs time out). Your goal is to re-architect the agent into a production-grade system. You will implement a Dual-Layer Memory system to persist user context across sessions and apply Reliability Patterns (Retries, Rate Limits, and Circuit Breakers) to ensure the agent remains robust even when external services fail.