Progressive disclosure is not reliable because LLMs are inherently lazy. "In 56% of eval cases, the skill was never invoked. The agent had access to the documentation but didn't use it." Vercel ran evals on Next.js 16 APIs that aren't in model training data to test whether agents could learn framework-specific knowledge through Skills vs. persistent context. Skills are the "correct" abstraction: package domain knowledge, let the agent invoke it when needed, minimal context. The agent decides when to retrieve. They work well WHEN the user triggers them; otherwise, LLMs just ignore them. Vercel's benchmarking is the first experiment of this kind I've seen, and it's actually interesting. - Baseline (no docs): 53% - Skill (default): 53% - Skill with explicit instructions: 79% - AGENTS[.]md with 8KB compressed docs index: 100% The skill approach assumes agents reliably recognize when they need external knowledge and act on it. They don't. "You MUST invoke the skill" made agents read docs first and miss project context. "Explore project first, then invoke" performed better. Same skill, different outcomes based on prompting. The winning approach removed the decision entirely. An 8KB compressed index embedded in AGENTS[.]md, with one instruction: "Prefer retrieval-led reasoning over pre-training-led reasoning." Two agent design learnings: 1. Passive context beats active retrieval for foundational knowledge. Don't make the agent decide to look things up, make the index always present. 2. Compress aggressively. Vercel went from 40KB to 8KB (80% reduction) with zero performance loss. The agent needs to know where to find docs, not have full content in context. The gap between "agent can access X" and "agent will access X" is larger than we assume. I keep seeing similar findings across agent architectures. Kimi Swarm's orchestrator is trained specifically to avoid sequential execution. Without training, orchestrators default to serial processing, planning a list of steps and executing them one by one. It's the EASY path. The agent defaults to the lazy path: hallucinating from training data rather than retrieving docs. Passive context removes the choice entirely; the agent doesn't decide whether to look things up; the index is already there. We keep finding that the "smarter", more autonomous design (let the agent decide when to X) underperforms the "dumber" design (always X, or structurally enforce X).