AI Agents in Java with Generative AI from Coursera

AI agents aren’t passive tools. They think, act, and solve problems—without waiting for instructions. That’s the future of software. And in this course, you’ll learn how to build it.

Frameworks come and go. Principles last. This course cuts through the noise to teach you how AI agents really work—using rock-solid Java.

Forget tutorials on trendy APIs that’ll be dead by next quarter. You’ll learn to build AI agents from the ground up. No fluff. No shortcuts. Just the core architecture that powers intelligent systems—knowledge that stays useful no matter how fast the landscape shifts.

In this course, you will:

- Master Java-based agent architectural fundamentals - Understand the core GAME components (Goals, Actions, Memory, Environment) that make AI agents tick and how they work together in a cohesive Java system

- Leverage Java's strengths for efficient agent development - Use Java's reflection, annotation processing, and strong typing to create robust, maintainable agent frameworks with minimal boilerplate code

- Rapidly prototype and implement Java agents - Learn techniques to quickly design Java agent capabilities with prompt engineering before writing a single line of code, then efficiently translate your designs into working Java implementations

- Connect Java AI agents to real-world systems - Build Java agents that can interact with file systems, APIs, and other external services

- Create Java-powered tool-using AI assistants - Develop Java agents that can analyze files, manage data, and automate complex workflows by combining LLM reasoning with Java's extensive libraries and functionality

- Build Java developer productivity agents - Create specialized Java agents that help you write code, generate tests, and produce documentation to accelerate your software development process

Why Principles Matter More Than Frameworks

The AI landscape is changing weekly, but the core principles of agent design remain constant. By understanding how to build agents from scratch, you'll gain:

- Transferable knowledge that works across any LLM or AI technology

- Deep debugging skills because you'll understand what's happening at every level

- Framework independence that frees you from dependency on third-party libraries and allows you to succeed with any of them

- Future-proof expertise that will still be relevant when today's popular tools are long forgotten

By the end of this course, you won't just know how to use AI agents—you'll know how to build them in Java, customize them, and deploy them to solve real business problems.

This course will teach you these concepts using OpenAI's APIs, which require paid access, but the principles and techniques can be adapted to other LLMs.

What's inside

Syllabus

Agentic AI Concepts

AI Agents, Tools, Actions, & Language

GAME: A Conceptual Framework for AI Agents

Career center

Learners who complete AI Agents in Java with Generative AI will develop knowledge and skills that may be useful to these careers:

AI Agent Developer

An AI Agent Developer designs, builds, and deploys autonomous software agents capable of thinking, acting, and solving problems. This course is exceptionally well-suited for aspiring AI Agent Developers, as it focuses entirely on teaching you how to build such agents in Java. You will master Java-based agent architectural fundamentals and understand the core GAME components—Goals, Actions, Memory, Environment—crucial for creating intelligent systems. The course emphasizes building agents from the ground up, moving beyond transient frameworks to provide future-proof expertise and deep debugging skills, ensuring you can customize and deploy robust Java agents to solve real business problems.

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Java Artificial Intelligence Software Engineer

A Java Artificial Intelligence Software Engineer develops and implements AI-driven solutions predominantly using the Java programming language. This course is ideal for a Java Artificial Intelligence Software Engineer, as it focuses on building AI agents directly in Java. You will leverage Java's strengths, including reflection, annotation processing, and strong typing, to create robust, maintainable agent frameworks with minimal boilerplate code. By learning to build intelligent systems from the ground up and mastering transferable knowledge, you will be equipped to develop sophisticated, performant AI applications that can interact with real-world systems like file systems and APIs, using a language you already know.

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Generative AI Engineer

A Generative AI Engineer specializes in developing and deploying applications that leverage large language models and other generative artificial intelligence technologies. This course is highly relevant for someone looking to become a Generative AI Engineer, as it teaches you to create Java-powered tool-using AI assistants. You will learn to combine LLM reasoning with Java's extensive libraries and functionality to analyze files, manage data, and automate complex workflows. By understanding how to rapidly prototype Java agent capabilities with prompt engineering before writing code, you gain crucial skills for efficiently translating design into working generative AI implementations.

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Intelligent Systems Architect

An Intelligent Systems Architect designs the overarching structure and blueprint for complex AI-driven systems, ensuring scalability, robustness, and maintainability. This course provides a strong foundation for an aspiring Intelligent Systems Architect by teaching agent architectural fundamentals. You will learn the core architecture that powers intelligent systems and how GAME components work together in a cohesive Java system. The emphasis on 'principles matter more than frameworks' cultivates framework independence and future-proof expertise, essential for designing systems that endure rapid technological shifts. This role typically requires significant experience, and an advanced degree is often preferred.

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Research Engineer Artificial Intelligence

A Research Engineer Artificial Intelligence explores new AI algorithms, develops experimental systems, and contributes to cutting-edge advancements in the field. This course is highly valuable for a Research Engineer Artificial Intelligence because of its emphasis on core principles over transient frameworks. By learning to build AI agents from the ground up, you gain transferable knowledge that works across any LLM or AI technology, crucial for exploratory development. The deep understanding of agent architectural fundamentals and GAME components prepares you to innovate and develop novel intelligent systems. This role typically requires an advanced degree, such as a Master's or PhD.

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Developer Productivity Engineer

A Developer Productivity Engineer focuses on building tools and systems that streamline software development workflows and improve efficiency for engineering teams. This course is highly beneficial for a Developer Productivity Engineer, as it explicitly teaches you to build Java developer productivity agents. You will create specialized Java agents that help write code, generate tests, and produce documentation, directly accelerating the software development process. The understanding of agent architectural fundamentals and how agents can automate complex workflows provides a unique skill set for enhancing developer experience and efficiency through intelligent automation.

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AI Solutions Integration Engineer

An AI Solutions Integration Engineer is responsible for connecting AI systems, including intelligent agents, with existing enterprise applications, databases, and external services to create cohesive and functional solutions. This course is particularly relevant for an AI Solutions Integration Engineer, as it covers connecting Java AI agents to real-world systems. You will learn to build Java agents that can interact with file systems, APIs, and other external services, a critical skill for seamless integration. The ability to create tool-using AI assistants empowers you to design robust connectors that bridge AI capabilities with diverse system environments.

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Automation Software Engineer

An Automation Software Engineer designs, develops, and implements software systems to automate various processes and tasks across an organization. This course is quite beneficial for an Automation Software Engineer, as it teaches you how AI agents 'think, act, and solve problems—without waiting for instructions,' which is the essence of advanced automation. You will learn to create Java-powered tool-using AI assistants that can automate complex workflows, leveraging LLM reasoning with Java's extensive libraries. This ability to build intelligent, autonomous agents directly supports the creation of sophisticated automation solutions.

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Documentation Automation Specialist

A Documentation Automation Specialist focuses on implementing tools and processes to automatically generate, update, and manage technical documentation for software products and systems. This course is directly relevant for a Documentation Automation Specialist, as you will learn to build Java developer productivity agents that can 'produce documentation' to accelerate the software development process. By mastering agent architectural fundamentals and understanding how to rapidly prototype agent capabilities, you gain the skills to design and implement intelligent systems specifically aimed at streamlining documentation workflows and ensuring content accuracy and consistency.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer focuses on the deployment, monitoring, and scaling of machine learning models and AI systems in production environments. While this course centers on building agents, the principles it imparts may be useful for a Machine Learning Operations Engineer. The course cultivates 'deep debugging skills' because you understand what is happening at every level of agent design, which is critical for troubleshooting live AI systems. Furthermore, its emphasis on building robust, maintainable agent frameworks using Java's strong typing contributes to creating reliable and production-ready AI applications. An advanced degree is common for this role.

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Systems Developer

A Systems Developer designs and builds complex software systems, often focusing on low-level components, performance, and integration. This course may be useful for a Systems Developer aiming to specialized in intelligent systems. It teaches how to use Java's reflection, annotation processing, and strong typing to create robust, maintainable agent frameworks with minimal boilerplate code, skills highly transferable to general systems development. The focus on core architecture and building from the ground up helps cultivate deep debugging skills and framework independence, crucial for developing resilient and high-performing system components for various applications.

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Backend Software Engineer

A Backend Software Engineer builds and maintains the server-side logic, databases, and APIs that power applications. This course may be useful for a Backend Software Engineer looking to enhance their skills with intelligent systems. It covers leveraging Java's strengths for efficient agent development, including reflection, annotation processing, and strong typing, which are valuable for creating robust, maintainable backend frameworks. The ability to connect Java AI agents to real-world systems, including other APIs, helps build a foundation for developing sophisticated and interconnected backend services that can incorporate intelligent functionalities.

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Software Quality Assurance Engineer

A Software Quality Assurance Engineer ensures the quality of software products through methodical testing, defect identification, and process improvement. This course may be useful for a Software Quality Assurance Engineer, especially one interested in test automation and advanced quality tools. The course explicitly teaches learners to build 'Java developer productivity agents' that can 'generate tests,' which directly applies to creating sophisticated automated testing frameworks. Understanding 'deep debugging skills' from building agents from scratch also enhances a quality assurance engineer's ability to thoroughly analyze and troubleshoot software systems.

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Data Applications Engineer

A Data Applications Engineer builds and maintains software applications that process, manage, and leverage data to deliver specific functionalities or insights. This course may be useful for a Data Applications Engineer interested in infusing intelligence into data-driven applications. It covers creating Java-powered tool-using AI assistants that can 'analyze files' and 'manage data' by combining LLM reasoning with Java's extensive libraries. Furthermore, the ability to connect Java AI agents to 'file systems' and 'APIs' helps build a foundation for developing intelligent data management and processing applications that go beyond traditional data pipelines.

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Technical Program Manager Artificial Intelligence

A Technical Program Manager Artificial Intelligence leads and oversees complex AI initiatives, coordinating engineering teams and ensuring project success. While not a hands-on development role, this course may be useful for a Technical Program Manager Artificial Intelligence. Understanding 'agent architectural fundamentals' and the core GAME components—Goals, Actions, Memory, Environment—provides invaluable insight into the technical aspects and feasibility of AI agent projects. Learning the course's perspective on 'rethinking how software is built in the age of AI Agents' can significantly enhance your ability to communicate effectively with technical teams and strategically plan AI development lifecycles.

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