The Rise of Agentic AI Search: How Glean’s Waldo Model Is Redefining Enterprise Search

In a bid to revolutionize enterprise search, Glean has unveiled its latest agentic AI search model, Waldo. Released on April 28, this reinforcement learning agentic search model is set to transform the way enterprises approach information retrieval and reasoning.

At its core, Waldo is built on Nvidia’s Nematron 3 Nano open model, which allows Glean to create a highly customizable and scalable search engine. The model was designed to perform the initial search function, handing over to a frontier model that retrieves and reasons. This approach enables enterprises to tap into the full potential of agentic AI, while minimizing the complexity and cost associated with large-scale search engines.

According to Rowan Curran, an analyst at Forrester Research, Waldo’s design is a significant departure from traditional search 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,” he said.

Glean’s pivot towards agentic search is a natural response to the evolving needs of enterprises. With Waldo, the Palo Alto-based vendor is positioning itself as a leader in the enterprise search market, leveraging its expertise in SaaS platforms to better understand the search patterns of its enterprise users.

However, while Glean’s specialized model has several advantages, it also poses some challenges. For instance, search and retrieval are complex tasks that require different approaches. There are various methods for retrieving data, such as using APIs or MCP connectors, each with its own strengths and weaknesses.

Bradley Shimmin, an analyst at Futurum Group, notes that these challenges are not unique to Glean’s Waldo model. “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,” he said.

Another challenge facing search vendors like Glean is the increasing diversity of enterprise demands. Over the past couple of years, enterprises have shifted from simply seeking “Google for my enterprise” to more complex requests, such as “Can you give me ChatGPT for my enterprise?” This change in expectations has forced vendors to reassess their value proposition and focus on delivering more specialized solutions.

However, Waldo’s release is a promising sign for the entire AI industry. The more vendors like Glean that push into these marketplaces with their own solutions, the better off the whole industry is," Shimmin said.

The impact of Waldo will be felt across various industries, from healthcare and finance to retail and education. As enterprises adopt agentic AI, they will require specialized search models that can navigate complex data landscapes and provide accurate results.

In conclusion, Glean’s Waldo model represents a significant shift in enterprise search, leveraging domain- and specialized-task-specific models to provide a more efficient and effective solution. While challenges remain, the benefits of Waldo are undeniable, positioning Glean as a leader in the enterprise search market.

Glean’s Approach to Agentic Search

Glean’s approach to agentic search is centered around the idea that domain-specific models are often more effective than generic frontier models. By creating a specialized model for search, Glean aims to help enterprises save on the cost of using large frontier models.

The Role of Nematron 3 Nano

Nematron 3 Nano is an open-source model developed by Nvidia that serves as the foundation for Glean’s Waldo model. By leveraging this model, Glean can create highly customizable and scalable search engines that are optimized for specific industries and use cases.

The Evolution of Enterprise Search

Enterprise search has undergone significant changes over the past few years, driven by the increasing adoption of agentic AI. What was once seen as a simple request for “Google for my enterprise” has evolved into more complex requests, such as “Can you give me ChatGPT for my enterprise?”

The Benefits of Agentic Search

Agentic search offers several benefits over traditional search models, including:

• Improved Accuracy: Agentic search models can navigate complex data landscapes and provide accurate results, reducing the risk of false positives or misinterpretation.
• Increased Efficiency: By leveraging domain- and specialized-task-specific models, agentic search solutions can streamline information retrieval processes, saving time and resources for enterprises.
• Enhanced Customization: Agantic search models enable enterprises to tailor their search solutions to specific needs, leveraging expertise in a particular area to drive business value.

The Future of Enterprise Search

As the AI industry continues to evolve, it is likely that enterprise search will undergo significant changes. The adoption of agentic search models like Waldo is expected to become more widespread, driven by the need for enterprises to navigate complex data landscapes and provide accurate results.

In this future landscape, Glean’s Waldo model is poised to play a leading role, providing enterprises with a highly customizable and scalable search engine that leverages domain- and specialized-task-specific models. As the AI industry continues to innovate, it will be exciting to see how Waldo and other agentic search models shape the future of information retrieval and reasoning.

The Impact of Agantic Search

Agantic search has far-reaching implications for various industries, from healthcare and finance to retail and education. By leveraging domain- and specialized-task-specific models, enterprises can streamline information retrieval processes, drive business value, and improve overall efficiency.

As agantic search continues to evolve, it is likely that we will see increased adoption across various industries, driven by the need for accurate results and efficient information retrieval. Glean’s Waldo model is poised to play a leading role in this evolution, providing enterprises with a highly customizable and scalable search engine that leverages domain- and specialized-task-specific models.

Conclusion

Glean’s Waldo model represents a significant shift in enterprise search, leveraging domain- and specialized-task-specific models to provide a more efficient and effective solution. By creating a specialized model for search, Glean aims to help enterprises save on the cost of using large frontier models.

While challenges remain, the benefits of Waldo are undeniable, positioning Glean as a leader in the enterprise search market. As the AI industry continues to innovate, it will be exciting to see how Waldo and other agantic search models shape the future of information retrieval and reasoning.

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