Automation in platform engineering is primarily about reducing repetitive manual work, or toil, which consumes engineering capacity and increases the risk of human error. Option A is correct because cluster autoscaling—adjusting resources to meet workload demand—is a repetitive, ongoing task that is better handled through automation. Automating this process ensures scalability, efficiency, and reliability while freeing platform teams to focus on higher-value work.

Option B may provide learning opportunities but is not a sustainable justification. Option C is subjective and inefficient, while Option D is overly broad—automation should be applied thoughtfully to tasks that bring measurable benefits.

Automating autoscaling aligns with cloud native best practices, ensuring workloads can respond elastically to demand changes while maintaining cost efficiency. This reduces manual overhead, improves resiliency, and supports the developer experience by ensuring resource availability.