Course Overview
This intensive training transforms experienced agile developers into AI-enabled practitioners who can dramatically accelerate development while maintaining engineering excellence. You’ll learn to integrate AI tools into test-driven development cycles, implement effective human-AI pair programming, and create rapid feedback loops that maintain quality at unprecedented speeds. Crucially, the course addresses the dark side of AI assistance: dangerous antipatterns, skills atrophy risks, and quality degradation that can destroy teams and codebases.
Through hands-on exercises and real-world scenarios, you’ll master the balance between AI acceleration and engineering rigor. The course emphasizes practical risk mitigation strategies, teaching you to identify when AI suggestions are harmful, maintain fundamental skills despite automation, and build team practices that leverage AI without creating dependencies. Perfect for teams ready to embrace AI-enhanced development while avoiding the pitfalls that have plagued early adopters.
Learning Objectives
- Apply AI tools to accelerate TDD red-green-refactor cycles effectively
- Design comprehensive test suites that validate AI-generated code
- Implement human-AI pair programming with proper guardrails
- Identify and avoid dangerous antipatterns in AI-assisted development
- Maintain engineering rigor and fundamental skills while using AI
- Create testing strategies specifically for AI-generated code
- Build resilient teams that thrive with AI without losing competencies
- Establish metrics that balance velocity gains with quality risks
Topics Covered
- AI-Enhanced XP/TDD Foundations - Paradigm shifts and unchanged principles
- Accelerated Test-Driven Development - AI-powered red-green-refactor cycles
- Risks and Antipatterns - Magic thinking, skills atrophy, and quality degradation
- Human-AI Pair Programming - Effective collaboration with proper judgment
- Engineering Excellence - Preserving craftsmanship and fundamental skills
- Testing AI-Generated Code - Trust boundaries and validation strategies
- Metrics and Monitoring - Balancing velocity with risk indicators
- Building Resilient Teams - Healthy practices and avoiding dysfunction
- Safe Implementation - Phased adoption with essential guardrails
What You Get
- Practical AI tool integration playbooks
- Antipattern identification checklists
- Risk mitigation frameworks
- Team health assessment tools
- Skills preservation program templates
- AI-specific testing strategies
- Metrics dashboards for balanced tracking
- Implementation roadmap with guardrails