Combine Test-Driven Development with AI-assisted engineering to build cleaner, more reliable software.

AI is transforming software development, but quality still depends on disciplined engineering practices. This immersive 2-day training helps teams use Google Antigravity alongside TDD, clean code, refactoring, and testing practices to improve software quality and maintainability.

  • Why get trained: Learn Red → Green → Refactor, AI-assisted testing, clean code, refactoring, and safe legacy system modernization using characterization tests.
  • Why it matters: Improve test coverage, reduce technical debt, maintain code quality, and accelerate development without losing engineering control.
  • Who should attend: Software Engineers, Full Stack Developers, Technical Leads, Engineering Managers, QA Automation Engineers, and Teams adopting AI-assisted engineering.

Gain hands-on experience through TDD exercises, AI-assisted implementation, testing and refactoring workflows, and practical engineering workshops. HRD Corp Claimable.

Overview

Software engineering is evolving rapidly as AI-assisted development environments become part of modern engineering workflows. Today’s engineering teams must deliver software faster while maintaining high standards of quality, scalability, maintainability, and engineering discipline. Organizations are under increasing pressure to improve test coverage, reduce technical debt, modernize legacy systems, and respond quickly to changing business needs.

At the same time, the way developers build software is also changing. Modern engineering teams are increasingly collaborating with AI systems throughout the software development lifecycle, from implementation and testing to debugging, refactoring, code review, and modernization activities.

This immersive 2-day training is designed to help engineering teams understand how to combine proven engineering practices with AI-assisted development workflows using Google Antigravity as an AI engineering environment.

The focus of the program is not AI-generated coding without control. Instead, the training emphasizes disciplined engineering practices supported by AI, where engineers remain responsible for architecture, design quality, testing strategy, maintainability, and delivery standards. Participants will learn how to effectively apply TDD with AI assistance, improve engineering quality and maintainability, safely modernize legacy systems, and use AI-assisted workflows for implementation, testing, and refactoring.

Skills Covered

  • Understand AI-assisted engineering workflows
  • Apply Test-Driven Development effectively
  • Use Red → Green → Refactor workflows consistently
  • Write maintainable and effective unit tests
  • Improve code quality through refactoring
  • Use Google Antigravity to support implementation and testing workflows
  • Apply clean code and engineering craftsmanship principles
  • Improve legacy systems safely using characterization tests
  • Reduce complexity, duplication, and technical debt
  • Integrate TDD into engineering delivery workflows
  • Build sustainable AI-assisted engineering practices

Prerequisites

Basic software development experience is recommended, but the training covers TDD concepts, workflows, and practical implementation from the ground up.

Target Audience

  • Software Engineers
  • Full Stack Developers
  • Technical Leads
  • Engineering Managers
  • QA Automation Engineers
  • Teams adopting AI-assisted engineering.

Course Curriculum

Module 1: AI-Assisted Engineering & Modern Development Workflows

This module introduces the shift toward AI-assisted software engineering and explains how modern AI development environments support engineering productivity, implementation workflows, testing, and refactoring. Participants learn how AI-assisted development changes engineering workflows while maintaining engineering accountability, code quality, and delivery discipline. The module also establishes the principles of responsible AI-assisted engineering and sustainable software delivery.

Module 2: Unit Testing Fundamentals & Engineering Quality

This module focuses on the foundational concepts behind effective unit testing and explains how testing improves software quality, maintainability, confidence, and delivery safety. Participants learn how high-quality test suites support long-term engineering sustainability while improving refactoring safety and development confidence. The module also introduces practical testing workflows commonly used in modern engineering environments.

Module 3: Test-Driven Development (TDD) in Practice

This module introduces Test-Driven Development as a disciplined engineering workflow for designing maintainable and evolvable software systems. Participants learn how the Red → Green → Refactor cycle improves software design, reduces implementation risk, and enables safer iterative development. The module focuses heavily on practical coding exercises and incremental feature development using test-first approaches.

Module 4: AI-Assisted Development Workflows with Google Antigravity

This module explores how Google Antigravity supports implementation, testing, debugging, and iterative development workflows within modern engineering environments. Participants learn how AI-assisted workflows can accelerate implementation while still maintaining engineering discipline, review practices, and code quality standards. The module also examines practical workflows for collaborating with AI in software development.

Module 5: Emergent Design, Refactoring & Clean Code

This module focuses on improving software design incrementally through refactoring, clean code practices, and engineering craftsmanship techniques. Participants learn how maintainable software systems evolve through continuous improvement rather than large upfront design efforts. The module emphasizes readability, simplicity, flexibility, and sustainable design practices supported by safe refactoring workflows.

Module 6: Working with Legacy Systems & Technical Debt

This module teaches practical approaches to safely improving legacy systems through characterization testing, dependency isolation, and incremental modernization strategies. Participants learn how to introduce testing into existing systems, gradually improve maintainability, and reduce technical debt without destabilizing production systems. The module also explores how AI-assisted analysis can support modernization and refactoring activities.

Module 7: Sustainable Engineering Workflows & CI/CD Quality Systems

This module examines how TDD integrates into modern software delivery pipelines and engineering quality systems. Participants explore how engineering teams maintain software quality through automated testing, quality gates, review workflows, and sustainable delivery practices. The module also introduces practical approaches for integrating AI-assisted development responsibly into engineering operations and CI/CD workflows.

Module 8: Capstone – AI-Assisted TDD Engineering Workflow

This capstone module brings together all training concepts through a hands-on implementation exercise where participants apply TDD, refactoring, clean code practices, and AI-assisted development workflows to build and improve a working software feature.

Dates & Locations

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Exam & Certification

Complete the AI-Assisted Test-Driven Development with Google Antigravity Training and receive your AI-Assisted Test-Driven Development with Google Antigravity Training Certificate of Completion after successfully completing the 2-day program. This validates your skills in applying AI-assisted engineering workflows, Test-Driven Development, refactoring, clean code, and sustainable software engineering practices using Google Antigravity.

Training & Certification Guide

Participants receive an AI-Assisted Test-Driven Development with Google Antigravity Training Certificate of Completion.

Why train with Trainocate

AI-Assisted Test-Driven Development combines traditional TDD practices with AI-powered development tools. Developers write tests first and use AI assistance to accelerate implementation, testing, refactoring, and debugging while maintaining engineering quality.

Google Antigravity is an AI-assisted engineering environment that helps developers with implementation, testing, debugging, refactoring, and software modernization activities. This training teaches practical ways to use it within disciplined engineering workflows.

Yes. Organizations increasingly value engineers who can combine strong software engineering practices with AI-assisted development. These skills can help professionals stay relevant and contribute more effectively to modern development teams.

TDD helps developers build reliable, maintainable, and well-tested software by writing tests before implementation. This approach reduces defects, improves design quality, and supports safer refactoring.

Yes. Participants learn how to identify technical debt, apply refactoring techniques, improve code quality, and modernize existing systems without disrupting production environments.

No. AI can support testing activities, but TDD remains an engineering discipline that guides software design, quality, and maintainability. AI works best when combined with strong engineering practices.

AI-assisted workflows can reduce repetitive tasks, accelerate implementation, improve testing efficiency, and support refactoring activities while allowing engineers to focus on higher-value work.

Yes. Participants learn characterization testing, dependency isolation techniques, and practical modernization approaches for improving legacy systems safely and incrementally.

AI can generate code quickly, but software quality still depends on testing, maintainability, and engineering discipline. TDD provides the structure needed to ensure AI-assisted development produces reliable and sustainable software.

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