When review
becomes the work.

A pattern I keep noticing is polished AI-assisted text being passed between teams before it has been adapted to the actual context. The missing context becomes someone else’s review task. When AI is used again to critique and revise the document, further suggestions can keep it circulating while documentation grows and implementation waits.

The difficulty here is repeated revision of the same work. I see it as a separate challenge from receiving many useful results in parallel: each round may look productive without bringing the document closer to a usable decision. This is an observation, not a claim that AI always finds a fault or that every review loop is unproductive.

I want the purpose, relevant context and acceptance criteria agreed before work begins. Reviewers need the same facts, constraints and intended use, together with a clear account of what the result must satisfy. Otherwise, each handover can introduce a different idea of what “good” means.

A plausible review suggestion is not yet evidence that a correction is necessary. Another round should address a material gap or new evidence. I want necessary corrections separated from optional refinements, with the requirement or evidence behind each proposed change made explicit.

My working hypothesis is that productivity gains depend on defining the workflow as well as limiting repeated reviews. Agree the steps, required checks and decision points first. Additional review can then respond to triggers such as high risk or criticality, handovers, contradictions, ambiguity or missing essential information. AI may help identify these triggers; required checks still apply if it misses one.

Define who decides, when the criteria are met and how unresolved gaps escalate after a bounded number of attempts. Reaching a limit should prompt a decision about the gap, not automatic acceptance. The aim is a usable decision and practical follow-through. I would assess the benefit through time to a usable result, rework and its effect in practice.

Anthropic describes evaluation-and-revision loops as suitable when evaluation criteria are clear and iteration adds measurable value. Its guidance also discusses evidence from the working environment, human checkpoints and stopping conditions. These are design principles, not evidence that this particular workflow will improve productivity.