Progressive disclosure
person concept tool org talk claim — click a node to jump to its page; hover an arrow for the relation
A design principle in AI agent engineering where instructions, context, or capabilities are surfaced to a model incrementally and at the moment of need, rather than being front-loaded all at once at the start of an interaction.
Core Principle: Just-in-Time Context Delivery
Ryan Lopopolo (OpenAI) advocates for progressive disclosure as a foundational property of well-built agent harnesses. His argument is that overwhelming an agent with all instructions upfront is counterproductive — instead, a good harness should defer or surface instructions precisely when they become relevant: "figuring out ways to either defer or just in time surface those instructions is kind of what a good harness should do." → This framing treats progressive disclosure not as a nice-to-have but as a defining characteristic of harness quality. →
Role in Agent Skills Architecture
Philipp Schmid (Google DeepMind) identifies progressive disclosure as the distinguishing structural property of agent skills specifically, contrasting them with other agent design patterns: "the big difference with skills is that they work on progressive disclosure." → This positions progressive disclosure as not merely an implementation detail but an architectural principle that defines what a skill is — capabilities that emerge and become available to the agent contextually, tied to the evals-driven development process Schmid advocates in the same talk.
Synthesis
Both speakers treat progressive disclosure as part of avoiding context pollution and reducing cognitive/computational load on the model at any given step. In harness engineering it manifests as deferred instruction injection; in skills architecture it manifests as capability gating. The shared intuition is that agents perform better when their working context is scoped to what is immediately actionable.