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

AI agents are failing because of bad data

Companies rushing to deploy AI agents are hitting a fundamental wall: the massive, disorganized pools of data inside their own enterprises. These agents, designed to automate complex business tasks, are failing because they can’t find the right information at the right time.

“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,” said Anish Mather, director of product management at Elastic.

The scale of the enterprise data problem

Enterprise data isn’t just large; it’s diverse and scattered. It includes documents, transactions, images, and multimodal content living across countless systems. An AI agent needs to merge these sources and understand their connections to function.

“You must be able to get answers across all of them,” Mather explained. This creates a core demand for specialized search technology that can provide precise context, not just a list of links.

How bad data causes compounding failures

The problem intensifies because AI agents perform multi-step processes. They need accurate data at each step to inform the next action. An error at the beginning compounds with every subsequent step.

“If there’s bad results at the first step, it just compounds at every step that the agent takes,” Mather said. This is critical when agents are handling high-impact actions like closing support tickets or generating customer reports.

The consequences of these failures include:

• Broken business processes and workflows.
• Inaccurate reports used for downstream decision-making.
• Eroded trust in AI systems and poor return on investment.

Enterprises are overwhelmed, not lacking, data

Analysts confirm this is a widespread industry challenge. “Enterprises are not short on data; they are overwhelmed by it,” said Paul Nashawaty, principal analyst at theCUBE Research.

He notes that when an agent pulls the wrong document or an outdated policy early in a workflow, that small miss can snowball into a major error. The value of modern AI agents is contingent on them being “grounded in accurate, context-rich information.”

Search as the foundational fix

The proposed solution centers on re-engineering enterprise search. It’s no longer just about retrieval; it’s about “engineering context.” Vendors like Elastic are building frameworks on top of their search engines to address this.

Elastic’s approach involves its Elasticsearch platform to store all structured, unstructured, and vector data. On top of this, its Elastic Agent Builder framework is designed to deliver the precise context an agent needs.

“It has the flexibility to tune that as needed,” Mather said, referring to the platform’s hybrid search capabilities. Nashawaty agrees, stating this combination of search, context engineering, and governance offers a path to “building AI agents that are both smarter and safer.”