Agent assistance was a learning ground before full automation
We turned the problem around. Instead of having consumers talk directly to bots, we gave recommendation tools to human agents. Agents had more tolerance for failures because they could ignore incorrect suggestions and still do their jobs, but when recommendations were correct they got value and could train the system. This was less stressful for building products. We could iterate and fail without ruining the customer experience. The technology wasn’t good enough yet for full automation, so this approach let us learn gradually. Once models improved, we could then turn it back to direct user interaction.
— How Ultimate evolved from hundreds of supervised models to UltimateGPT · The Edge