• •Stefan Wolpers suggests using retrospective structures to evaluate AI system performance
• •The framework adapts team meeting practices to decompose AI tasks for bias and accuracy
• •It remains unclear how these manual audit processes scale as agentic AI workflows increase in complexity

Stefan Wolpers proposes repurposing Agile retrospective techniques to audit AI outputs by treating model interactions as iterative team tasks. Unlike traditional software audits that focus on code quality, this approach emphasizes the human-AI feedback loop to identify logic errors and drift. However, the methodology requires significant time investment, which may conflict with the speed benefits often promised by AI integration. Adopting this manual oversight could prove essential for maintaining operational reliability as reliance on autonomous systems grows.