When you keep breaking a task into smaller steps, at what point does it stop helping and start wasting your time?

This explores the point where breaking work into smaller pieces stops paying off and starts producing busywork. The corpus suggests the answer depends less on how many pieces you make and more on whether the cuts fall in the right places. One retrieval study found that getting the number of steps right fixes most of the problem. Standard LLM decomposition recovers only about a third of the steps that actually matter, and correcting the step count alone recovers 75% of the gains from more elaborate iterative methods What blocks skill retrieval in task decomposition?. Too coarse and you miss skills. Too fine and you're retrieving against steps that don't correspond to anything real. Busywork, in this framing, is decomposition at the wrong granularity.

The clearest stopping rule in the collection comes from LLM evaluation, where it's easy to keep splitting criteria forever. The Evalet workflow says to start fine-grained, keep refining, and zoom out to holistic scores once the clusters of similar judgments stop shifting between rounds When should practitioners stop decomposing LLM evaluations?. That's a useful general test: decomposition is still helping while it keeps revealing new structure. Once each round mostly reshuffles what you already found, you're producing activity rather than insight.

The failures appear when the connections between pieces carry the meaning. Fixed-size chunking in RAG does exactly this to how-to documents: it cuts procedures into fragments that lose their prerequisites and their order. The fix is not to cut less but to cut differently, into 'logic units' that each carry a prerequisite and an explicit link to the next step How do logic units preserve procedural coherence better than chunks?. Safety research shows the darker version. In multi-agent systems, a harmful goal can be split into subtasks that each look innocent, so the harm exists only in how they combine Can task decomposition hide harmful intent across agents?. Over-decomposition doesn't just waste effort. It can hide what the whole thing is doing.

The kind of split matters too. Separating the planner from the doer improves accuracy, and the planning skill transfers across domains while the solving skill doesn't Does separating planning from execution improve reasoning accuracy?. Recursive subtask trees with memory pruning let a single model handle reasoning that people usually spread across several agents Can recursive subtask trees overcome context window limits?. That raises a question: how much multi-agent splitting is structure, and how much is coordination overhead?

The part you might not expect comes from workplace research. When automation takes over some pieces of a job, the work that remains doesn't disappear. It changes shape. At one shipbroking firm, manual classification turned into auditing and correcting the algorithm Does automation eliminate manual work or repurpose it?. Economists argue that when tasks multiply together rather than add up, automating some of them makes the remaining bottleneck tasks more valuable Does automating some tasks actually increase the value of remaining work?, and AI shrinks the 'execute' layer of knowledge work while deciding and delivering grow Does AI really compress all layers of knowledge work equally?. So decomposing work for AI tends to push effort into checking and putting the pieces back together. That work only becomes busywork if nobody planned for it. The collection doesn't have a direct study of decomposition overhead in human workflows, so treat this last link as an open question rather than a settled finding.