Human Checkpoints Keep AI Agent Work Aligned
Fast agent workflows create more room for iteration, but human judgement still has to redirect them before small choices compound into drift.
Developed from a conversation between Pete Winn and Andy David
The first useful output can change the destination of an AI agent workflow. A plan may look settled in the abstract, then shift as soon as a draft makes its choices tangible. The human role isn’t to specify every decision in advance. It’s to see what the agent has made, recognise what should change and carry that judgement into the next cycle.
Pete described a practical design rhythm. Ask an agent for 20 examples, select the three strongest, identify the elements worth keeping from each and request rebuilt versions. The agent gets enough uninterrupted time to produce a meaningful batch, while the person supplies taste at defined review points. This avoids trying to follow every rapid change while still keeping the work pointed in the intended direction.
The danger grows when an agent runs unattended for a day and makes many consequential choices along the way. A large volume of completed work doesn’t show whether those choices were good, and reviewing it only at the end can make the drift expensive to unwind. Shorter, bounded cycles keep the decisions visible while there’s still time to redirect them.