Small mistakes at each step of a handed-off job look harmless alone, so how do they quietly add up to real damage?

This explores why tiny, occasional mistakes at individual steps of a long, handed-off task pile up into serious damage without anyone noticing. The corpus suggests the errors stay hidden because every place we normally look is either too local or too global to see them.

The clearest evidence is a study of long relay workflows, where a document is passed from step to step. Even the strongest frontier models degraded about 25% of the document's content, and the damage decelerated but never plateaued. Spot-checked outputs still looked fine (Do frontier LLMs silently corrupt documents in long workflows?). No single step fails visibly. Each one changes only a little, so any one output passes inspection. The harm exists only in the running total, and nobody inspects that.

Three other notes in the corpus describe the same blind spot from different angles. Per-action guardrails are structurally unable to state a rule that depends on history, so a series of individually permissible actions can add up to a violation no single check flags (Can stateless checks ever catch sequence-level constraint violations?). Scoring only the final answer misses most failures, because most are process violations rather than wrong answers. Adding intermediate verification raised task success from 32% to 87% (Where do reasoning agents actually fail during long traces?). Averaging confidence over a whole reasoning trace also dilutes the signal: a local breakdown gets washed out, while step-level confidence catches it (Does step-level confidence outperform global averaging for trace filtering?). A sparse error is invisible up close (each step looks fine) and invisible from far away (the total looks plausible).

The fixes all put a check at every step rather than only at the end. One approach makes verification run asynchronously alongside generation. It steps in only when it finds a violation, and on correct runs it adds almost no delay (Can verifiers monitor reasoning without slowing generation down?). Another goes to the extreme, splitting a task into minimal subtasks with voting on each step and flagging correlated errors. This ran a million-step task with zero errors, and small non-reasoning models were enough (Can extreme task decomposition enable reliable execution at million-step scale?). A milder version wraps the model in an explicit algorithm that gives each call only the context its step needs, so each step is a small, debuggable unit (Can algorithms control LLM reasoning better than LLMs alone?).

None of these notes explains why models introduce sparse errors in the first place. The corpus does show that once the errors exist, delegation hides them, and that the remedy is to check at every step instead of trusting the final result.