Progressive disclosure in agents doesn't scale.
And its benefits seems agent harness dependent.
（bookmark this one）
Finally there is a proper study on using agent skills and the effect of progressive disclosure.
Progressive disclosure is the agent skills pattern where you hand an agent a document path and let it decide what to read， from a short description down to specific passages.
Practitioners adopted it fast for book-length tasks， purely on vibes.
Researchers ran it across three agent harnesses and three model families on InfiniteBench.
On a single book：
the gain is harness-dependent， large when the agent navigates raw documents poorly， near zero when a strong harness already retrieves on its own.
Scale to many books and raw navigation falls apart while one level of disclosure pulls ahead. A second routing level never helps and sometimes breaks accuracy.
It feels like progressive disclosure buys context， but not intelligence. It is redundant while a strong agent can find the passage itself， and decisive once the corpus is too large to read.
Paper： https://arxiv.org/abs/2607.17598
Learn to build effective AI agents in our academy： https://academy.dair.ai/