Five Questions for Developers Collaborating with AI: Direction, Independent Execution, Verification, Delegation, and Learning

• In AI collaboration, determining direction, the basis for independent execution, verifying results, trust-based delegation, and assetizing learning have emerged as core questions
• Clarifying repository and service boundaries, as well as isolating tools and execution environments, are prerequisites for AI's independent work and parallel processing
• Fast feedback loops and small change units increase verification efficiency, and mutation testing must be used to identify verification gaps
• Expand task delegation to trusted targets, but set irreversible executions as human intervention points
• The goal is to reduce repetitive tasks by reusing the reasons for decisions and defaults through ADRs and shared infrastructure

Independent Execution and Structure

• Actual isolation at the repository, service, and resource level, rather than folder-level separation, reduces coupling
• Initial stack and interface choices determine subsequent iteration costs, so goal suitability and change costs must be considered together

Verification and Observation

• Existing tools such as SWC and Turborepo reduce build and test times, shortening the feedback cycle
• Securing visibility through failure logs, execution history, and dry runs provides the basis for bottleneck analysis and intervention point decisions

Delegation and Learning

• Expand the scope of delegation by linking the successes and failures of trusted targets such as code review bots and monitoring agents
• Document the learning from tacit knowledge moments of 'discomfort,' and measure learning effectiveness by the reduction of repetitive questions and rework