Building with AI-DLC using Amazon Q Developer

This article demonstrates how to build applications using AWS's AI-Driven Development Life Cycle (AI-DLC) methodology with Amazon Q Developer, using a River Crossing Puzzle web app as an example.

• AI-DLC methodology assigns routine tasks to AI while maintaining human oversight for critical decisions
• Workflow has three phases: Inception (planning), Construction (design/implementation), Operations (deployment/monitoring)
• Adaptive workflow skips unnecessary stages for simple projects, maintains rigor for complex ones
• Workspace Detection determines if project is greenfield or brownfield application
• Requirements Analysis generates clarifying questions in multiple-choice format with open-ended options
• Workflow Planning creates execution plan recommending which stages to execute or skip
• Code Generation Planning breaks down process into explicit numbered steps before actual coding
• Code Generation executes approved plan step-by-step with progress tracking and checkboxes
• Build and Test stage creates comprehensive instructions for building, packaging, and testing
• Human-in-the-loop behavior enforced throughout with user approvals at critical checkpoints
• Complete audit trails maintained for traceability and transparency
• AI-DLC workflow available as open-source on GitHub with Amazon Q Developer IDE plugin

AI-DLC balances AI automation with human oversight, enabling developers to leverage generative AI while maintaining control over architectural decisions and implementation approaches.