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by softwaredoug·10d ago·view on hn ↗
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...

3 comments
Thank you for the link! Super interesting and I appreciate how it was written.

It seems like a lot of the problems I have been running into with RAG on large/complex documents with generally low contrast in the information is not one that has been perfectly solved yet - here I am thinking I'd been a bit behind.

It's just unfortunate that none of the cloud providers are flexible enough to deal with the pace of change. Probably going to have to shove one of those 8b~ models into an instance to use when needed.

It's interesting you mention late interaction (retrieval), I had recently been using ChatGPT as a mirror to throw ideas back at me on this issue and had been musing about how nice it would be to have some sort of hierarchical embeddings that capture a whole chunk, then sentences and then sentence fragments or individual word and it seems that that fits the bill!

+1 on SID-1. we were definitely inspired by that paper
SID-1 co-author here: was a lot of fun writing it. want to chat? just firstname at domain.
yup! email sent!