How to ground AI agents in accurate, context-rich data

AI agents are all the rage in enterprises today. CEOs and CTOs want them brought into their businesses ASAP to perform a wide range of tasks that can streamline operations while boosting sales, revenue, and productivity.

But building and deploying effective and laser-focused AI agents takes more than desire. It requires an enormous and reliable firehose of critical business data that provides the lifeblood for effective agentic AI development and use inside enterprises.

Building and operating AI agents using unorganized data is like trying to navigate a rolling dinghy in a stormy ocean of 100-foot-tall waves.

Yet it turns out that supplying massive amounts of relevant data is still not enough if it cannot be accurately sorted, prioritized, organized, and made instantly available to the AI agents being built to deliver business value.

The challenge of unorganized enterprise data

Building and operating AI agents using unorganized data is like trying to navigate a rolling dinghy in a stormy ocean of 100-foot-tall waves.

Solving this conundrum is one of the most important tasks for companies today, as they struggle to empower their AI agents to reliably work as designed and expected. To succeed, this firehose of unsorted data must be put into the right contexts so that enterprises can use and process it correctly and quickly to deliver the desired business results.

This is where incredibly specialized search can help solve these massive challenges, says Anish Mather, the director of product management at Elastic, an open source AI search platform vendor.

“AI search isn’t just a search box that you type into and get a list of links,” said Mather. “It is a critically specialized, empowering technology that allows you to find the relevant information in this vast pool of data that lives within most enterprises.”

Why AI agents need the right data at the right time

The huge volume and enormous scale of the data inside a typical enterprise is what defines the challenge AI agents face as they are deployed by businesses, said Mather.

“There’s all kinds of data,” he said. “There’s document data, transaction data, there’s images and multimodal data that live across the enterprise. You must deal with all these diverse types of data that live in multiple places and be able to get answers across all of them. You must be able to merge data sources together, understand where they are connected, and how they relate to each other.”

That puts greater demands on agents and their need for a constant and diverse flow of the right data at just the right time, said Mather.

“All of those challenges can be addressed by search in different forms and capabilities,” he said. “It is a core need for agents to be able to get the right information and context that they need to do the job. That is the core of how an agent can be successful, especially in the enterprise space.”

How inaccurate data leads to compounding failures

Adding to the data demands is that AI agents can perform multiple steps or processes at a time while working on a task. But those concurrent and consecutive capabilities can require multiple streams of data, adding to the massive data pressures using search.

“What that means is that at each of those steps, there’s an opportunity to find some relevant data, use that data in a meaningful way, and take the next action based on the results,” Mather explained. “So, the importance of the relevance at each step becomes paramount. If there’s bad results at the first step, it just compounds at every step that the agent takes.”

The consequences are especially problematic when enterprises are trying to use AI agents to drive a business process or take meaningful actions within an application.

“Those are things like you want to close a support ticket, send an email to a customer, or you want to generate a report that’s going to be used for some downstream process,” Mather said. “All of those things are high impact. And the need to get those right, to have high accuracy, the right context [are] super important.”

This compounding problem of data flow and the immense scaling needs of AI agents are two of the biggest challenges in building and using agents today, he said. Even small relevance errors compound quickly, making robust search, security controls and continuous evaluation essential for accuracy, trust and return on investment (ROI).

To solve these kinds of agentic AI data challenges for enterprises, Elastic created its Elastic Agent Builder framework, which is a new layer within the company’s Elasticsearch distributed search and analytics engine and data retrieval platform.

Enterprises can use Elasticsearch to store all their structured, unstructured, and vector data. This allows them to use that data to build AI agents and other applications without the performance and accuracy hits they are experiencing today.

“Elastic has a robust set of capabilities to allow you to search that data in many ways, whether it’s semantic searching, lexical searching, or using hybrid search capabilities,” said Mather. “It has the flexibility to tune that as needed.”

Why enterprises are overwhelmed by data

The problems of supplying huge volumes of business data to AI agents are affecting companies across industries, said Paul Nashawaty, principal analyst for AppDev and modernization at theCUBE Research.

“As AI agents start doing real work inside the enterprise, not just answering questions but executing multistep, business-critical tasks, the issue of context becomes impossible to ignore,” he said. “Modern AI agents only deliver value when they’re grounded in accurate, context-rich information, and search is the foundation that makes that possible.”

Many organizations are eager to scale agents, according to Nashawaty’s research, “but many hit a wall when those agents are dropped into messy, noisy data environments. Enterprises are not short on data; they are overwhelmed by it. And when an agent pulls the wrong document, outdated policy or incomplete signal early in a workflow, that small miss can quickly snowball into a bad decision or a broken process.”

The result of these past shortcomings and failures, he said, is that search has become foundational for AI agents.

“It’s the mechanism that helps them separate signal from noise and stay grounded in reality,” he said. “What is changing now is how enterprises think about search itself. It is no longer just about finding documents; it is about engineering context. This is the problem that vendors like Elastic are focused on addressing, using search and agent tooling to help organizations ground AI agents more reliably as they move from experimentation to production.”

The role of search in delivering context to AI agents

By layering Elastic Agent Builder on top of Elasticsearch, the company’s approach directly addresses these challenges by empowering organizations to design and deliver the right context to each agent as required, said Nashawaty.

“This aligns directly with what theCUBE Research identifies as the biggest hurdle for enterprises: governance and trust,” he said. “Elastic’s combination of strong search, context engineering, and governance gives organizations a clearer path to building AI agents that are both smarter and safer.”

To learn more about how Elastic can help unlock the power of AI workflows and AI agents, read the ebook.

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