Agentic Search: The Foundation of Successful AI Agents
Stephen R. Balzac -
Put on a blindfold at home, and you’ll probably manage just fine—barring the occasional stray Lego. Familiarity can carry you a long way, at least until you want to cook or rearrange furniture.
But blindfold yourself on a busy sidewalk, and you’re suddenly a pinball, bouncing unpredictably without scoring points (and possibly getting flattened).
We need information about the world in order to move about freely. The same is true of AI agents.
AI agents sound extremely impressive: autonomous actors that can use independent reasoning and planning to carry out instructions, make decisions, and perform certain actions, all without user involvement. Sounds great! Discussions of AI agents frequently use the example of planning a vacation: tell the AI what you’d like, give it some broad parameters (location, activities, foods you like to eat on vacation, price range) and it will figure out the right vacation spot, find you hotel rooms, book tickets, and so forth. Personally, I’m not sure I’d trust an AI agent quite that far, but the concept makes sense: take reasonably predictable workflows and automate them. We can do some of this already, but adding AI to the mix expands the range of tasks that can be automated.
The catch (you knew there was a catch, right?) is that it’s not nearly as easy as it’s often made out to be. While it’s easy to find articles on the technical challenges of building AI agents, a point that is often overlooked is how the AI will “see” where it’s going.
Information is the Key to Agentic AI
Rapid, accurate access to relevant information is the key to making agents work. Without accurate information, the AI agent is no different from you or me walking around in a blindfold. For simple tasks in a familiar environment, we can get by, but walking down the street wearing a blindfold? Not so good. Actually, the agent is worse off than we would be: we have other senses that can provide valuable data about the environment, the agent has only one information channel. Therefore, it’s vital that agents can access information as they need it and that the information be accurate and relevant. Otherwise, you may think the agent is sending you to Paris only to find yourself in Paris, Missouri. Providing agents with information is where agentic search comes in.
What is Agentic Search?
Though it may sound a bit silly, agentic search is merely search done at the request of an agent. Just as with a person looking for information, agents need to be able to request relevant data and trust what they get back. An agentic architecture needs to support search capabilities that return trusted, reliable information without requiring you to redesign your entire technological or data ecosystem.
The search framework should:
- Search data where it lives (Zero ETL). Copying all your data into a vector database is complex, expensive, and time consuming. The whole point of an AI agent is to do work for you. The best way to do that is for agents to access your structured and unstructured data wherever it is and search all locations simultaneously.
- Integrate with existing security protocols. Your agent should only see what you’re allowed to see. Integration with existing security protocols prevents unauthorized data access and avoids adding yet another security layer to manage.
- Use AI to evaluate, dedupe, and rank results. An intelligent agent needs intelligent results.
In addition, the search framework should be easy to install in your environment. It shouldn’t take months to set up. That’s part of why search should be Zero ETL: minimizing changes to your existing environment is faster and safer. How long should it take to install an AI search framework? A week, preferably less.
Sounds Good. But Does Such a Search Solution Exist?
Fortunately, yes (I’ll bet you never saw that coming 😊 ). SWIRL AI Search is a complete AI search solution for internal corporate data (and external data as well). SWIRL can work on its own as a powerful, intuitive, universal search platform for users, or it can be part of an agentic architecture. As a search framework in an agentic architecture, SWIRL acts as a data orchestration layer that empowers agents to find the data they need to do their jobs.
Because SWIRL is designed to deduce context around queries, it can figure out that you’re attending a conference in Paris, France and not Paris, Missouri so you don’t end up in the wrong Paris (sadly, it will also figure out when the opposite is true. C’est la vie!).
AI agents require a robust, reliable, and comprehensive search framework in order to be useful. As a key component of agentic architecture, SWIRL provides relevant, secure, real-time access to enterprise knowledge for AI agents—without migrating, duplicating, or risking your data.