We built a retrieval system over 100 million web documents. The biggest lesson was not about any single configuration. When every experiment takes 30 hours, the speed of your reindex-evaluate loop determines how good your system can get.
Context windows can hold more than ever, but more context doesn't mean better answers. Here's why retrieval still matters, especially for agents.
Search has a new user: agents. Longer, programmatic queries change the performance math of keyword retrieval. Here we take a closer look at Block-Max WAND when querying is driven by agents.
In agentic systems, retrieval is context injection. Better retrievers produce more convincing distractors; better reasoners trust those distractors more deeply. This is Mutually Assured Distraction.
What happens when retrieval becomes verifiable? Agents stop just querying data and start improving their own context. This post breaks down how Hornet's schema-first, verifiable APIs enable self-optimizing systems built for agentic retrieval.
The agentic shift is changing infrastructure. Agents need more than access to data; they need highly relevant context at scale. This article makes the case for a new retrieval engine built to power the next generation of agentic systems.
