Web Search Designed for Agents

darian314 · x · 2026-07-16

This thread explains how Linkup rebuilt web search as an infrastructure built "for agents," rather than a search results page for humans.

They focus on atomic indexing: breaking down page information into finer granularities, claiming it is 10–100 times more detailed than traditional page-level indexing.
The goal is to let agents directly access more credible raw data, avoiding the long and expensive pipeline of "LLM → search → SERP API → scraping → cleaning → chunking → reranking."
The author uses an "airplane mode" analogy: after training, a model is effectively offline and cannot naturally access new information past its cutoff, whereas agents need continuous, up-to-date data.
The thread lists several use cases: autonomously running e-commerce, deep research, legal AI platforms, AI SDRs, and industrial and social products.
It also mentions an event case: expecting a few hundred registrations, they received 1200+ applications within 24 hours. The organizers used a small AI on Linkup to conduct deep research and score all applicants.

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