Enterprise search vendor Glean’s new agentic AI search model underscores the importance of enterprises using domain- and specialized-task-specific models, but it also illuminates the challenge facing search vendors.
Waldo, released on April 28, is a reinforcement learning agentic search model that runs before a frontier model is used. Its job is to search, and once it has finished, Waldo hands off to a frontier model that retrieves and reasons. According to Glean, Waldo searches and the frontier model generate the response. The model was built on Nvidia’s Nemotron 3 Nano open model.
With Waldo, Glean is making the case to enterprises that, since many agents first have to search for information before performing the tasks that they’re being asked to do, in that sense, search underlies all agentic applications.
So, it is better to have a search-specific model that performs the search, then use a reasoning model for reasoning and retrieval. Glean’s approach is based on the popular idea that domain-specific models are often more effective than generic frontier models and Glean says it aims to help enterprises save on the cost of using large frontier models.
“As enterprises start to get to the second and third generation of their agents, they’re starting to get more concerned about what the actual cost, effectiveness and performance is of these things,” said Rowan Curran, an analyst at Forrester Research. “There is an increasing appeal for these more directed models for tasks within overall agentic workflows and executions.”
With Glean being at its core an enterprise search vendor, the pivot to helping enterprises achieve the most accurate, direct retrieval of information is natural in the age of agentic AI. Other search vendors, such as Moveworks and Genspark, have had to move toward agentic search as well.
“Companies that have some expertise in a given area … be that through software or through professional services or through any service are in a position to translate that knowledge into cash using a targeted model," said Bradley Shimmin, an analyst at Futurum Group. He added that those vendors could also fine-tune or distill a model, which is what Glean did by using Nemotron 3 Nano.
An Upper Hand with a Downside
However, Glean’s specialized model stands out and could be a differentiating factor for the Palo Alto-based vendor, Curran said.
“They may have some advantage given that they run as a SaaS platform in terms of understanding the way that their enterprise users search more deeply than some of the other providers,” he said.
He added that search and retrieval will likely not be quite as simple as Glean has presented it, with a specialized model for search and another for retrieval and reasoning. For one, there are different ways to retrieve data, such as using APIs or MCP connectors. Also, frontier models have become adept at direct file access, a method in which the model can read, navigate and analyze files without much user input. Curran added that, six or eight months ago, it would have been unlikely for frontier models to look through an enterprise directory to find an answer that matched the query presented.
“Now you do have these capabilities in most of the frontier models as well as the harnesses that are being built around them,” Curran continued.
Another challenge that Glean and other search vendors face is that enterprises are making increasingly diverse demands.
“Over the past couple of years, we have gone from … ‘Can you give me Google for my enterprise’ when they were talking about high-end enterprise search, which is now transformed into ‘Can you give me ChatGPT for my enterprise?’” Curran said. The change in what enterprises are looking for, along with search being a fundamental part of agentic AI, means that vendors like Glean are reconsidering their value proposition, he added.
However, Waldo’s release is a promising sign for the entire AI industry, Shimmin said.
“The more vendors like Glean that push into these marketplaces with their own solutions, the better off the whole industry is,” he said.
