Agentic Coding: Full-Stack Java Apps with Cursor and Copilot transforms your software development workflow by teaching you to collaborate with AI coding agents as autonomous partners in building, debugging, and maintaining enterprise-grade Java applications. Instead of treating AI as a passive autocomplete tool or prompt-response chatbot, it shows you how to leverage Cursor’s agentic mode and GitHub Copilot’s advanced features to delegate complex tasks like refactoring legacy code, generating test suites, designing APIs, and resolving cross-file dependencies while retaining architectural control and code quality standards. You get practical strategies for integrating AI agents into full-stack Java development (Spring Boot, React/Angular frontends, databases) that accelerate delivery without sacrificing maintainability, security, or team alignment.

This Course Offers

• Agentic IDE workflow mastery: Learn configuring Cursor and Copilot for Java projects, setting up context-aware rules files, and structuring prompts that enable reliable multi-step reasoning across large codebases
• Full-stack Java acceleration techniques: Master using agents to scaffold Spring Boot services, generate JPA entities from schemas, create REST endpoints with validation, and build frontend components aligned with backend contracts
• AI-assisted testing and debugging: Understand delegating unit/integration test generation, interpreting stack traces for root cause analysis, and validating agent outputs against existing test coverage and quality gates
• Governance and collaboration practices: Develop skills reviewing AI-generated code critically, establishing team conventions for agent usage, documenting decisions, and ensuring compliance with security scanning and code review policies

Why We Love This Course

1. The focus on professional-grade agentic development makes this highly relevant beyond hobbyist experimentation. It feels like learning from a senior Java engineer who has shipped production systems with AI assistance and knows where agents amplify expertise versus introduce technical debt.
2. Real project workflows make abstract concepts tangible. You refactor a monolithic service into microservices with agent help, generate comprehensive tests for legacy modules, and debug a distributed transaction issue collaboratively, seeing measurable velocity gains without quality erosion.
3. Coverage of both technical execution and team integration provides sustainable adoption guidance. This is useful whether you are adopting AI individually or leading organizational transformation with guardrails.
4. The instructor brings credible experience in enterprise Java development and AI-augmented engineering. The approach emphasizes intentionality, verification, and long-term maintainability over speed-at-all-costs, ensuring you build systems that endure beyond the demo phase.

The future of coding isn’t replacement; it’s elevated collaboration. The question is whether you want to use AI as a crutch or master the disciplined partnership that turns agents into force multipliers for serious software craftsmanship. This course provides the essential foundation to excel in agentic Java development, helping you ship better software faster while preserving the integrity that defines professional engineering.

• Java developers seeking to integrate AI agents into daily workflows without compromising code quality
• Tech leads and architects establishing team standards for agentic development in enterprise environments
• Full-stack engineers accelerating delivery of Spring Boot + React/Angular applications with AI assistance
• DevOps and QA professionals collaborating with developers on AI-augmented testing and deployment pipelines

• Intermediate Java and Spring Boot proficiency is required as this is not an intro to Java development
• Familiarity with Git, IDEs, and basic full-stack concepts (REST, databases, frontend frameworks) is essential
• Access to Cursor Pro or GitHub Copilot subscription plus a Java development environment supports hands-on exercises
• Commitment to critical review and testing of AI outputs is non-negotiable for responsible adoption

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.