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People are building agentic search one of three ways:

1. Actually good retireval. There’s been a lot of progress on serving the kinds of queries agents tend to serve, from places like Hornet, MoxedBread, LightOn. Particularly in late interaction

2. Smarter harnesses with models/judges validating the result. This is now just seen as the generator/ evaluator pattern. Here’s where people try to just use grep or some other naive retrieval system. Let the agent figure it out. But it’ll consume a lot of tokens to get good results as it iterates and loops.

3. A model trained for retrieval. Give it dumb retriever like in (2) but it is fine tuned on the task as in (1).

This article is 3. But we’ve been seeing this all year with SID.ai, Gleans Waldo model etc. if this interests you I’d check those out, particularly SID.

I wrote about these 3 approaches here https://softwaredoug.com/blog/2026/06/08/three-kinds-of-agen...

+1 on SID-1. we were definitely inspired by that paper
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