What is agentic search? Nobody knows


Agentic search gets interesting when agents do not know how to find the right answer.

Oh, the agent might think it knows. It might confidently BS us. But the agent’s poor domain intuition steers itself astray.

Agents make false assumptions about what our users think is relevant. Our fashionista users think “red shoes” should return high-heels. When I worked at one company ABE wasn’t a president, it was an A/B testing tool. Agents need context to know these things - and context engineering needs agentic search

Confusingly, depending who you talk to, agentic search can mean one of three distinct implementation patterns:

  • Retrieval-centric - just build good search so agents can use it to fill in missing context
  • Harness-centric - steer the agent towards needed context, even with bad search
  • Model-centric - fine tune an LLM to know how to search our data

In this post, I’ll walk through each. With a bit more breadth, you might appreciate which flavor your colleagues seem to mean when they say “agentic search”

Keep reading on my blog

https://softwaredoug.com/blog/2026/06/08/three-kinds-of-agentic-search

-Doug

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I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.

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