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AI Agents Are Only as Smart as Their Data

Here's a line from UiPath that lands harder than it should: even the smartest AI agent in your organization can't answer a question as simple as "has our biggest customer paid their last invoice?" or "which support tickets are overdue in a given region?" It's not that the model isn't capable enough. It's that its grounding — the knowledge it can actually draw on — is only as good as the data it can securely reach. And for most AI agents today, that grounding stops at documents.

The Problem Sits at the "Grounding Wall"

Most enterprise AI deployments start with unstructured data: PDFs, knowledge base articles, wikis, files scattered across shared drives. This pattern, known as document grounding, works well for questions like "what does our return policy say?" The system retrieves the relevant snippets and feeds them into the model's context.

But that approach hits a wall the moment the question shifts to something like "which support tickets are overdue in the West region?" Questions like this depend on live operational data and the relationships between records — not documents. The answers live scattered across CRMs, ERPs, ticketing platforms, data warehouses, and operational databases, all of which change constantly and all of which need to stay tightly governed. A document-only grounding layer simply can't produce a complete, current picture.

Dumping Everything into a Vector Store Isn't the Fix Either

The obvious temptation is to say: "fine, let's just load all our operational data into a vector database and let the AI search through it." UiPath is fairly blunt about why that doesn't work. Vector search is good at finding information that's semantically similar, but most business questions actually require precise filtering, joins across tables, calculations, and consistent business definitions — not just a search for meaning.

They point to four things an agent actually needs to be properly grounded in real business data:

• Live, near-real-time access. The agent needs to see data as it stands right now, not as it looked in last night's extract. Many of the answers that matter change hourly, not weekly.

• Structured precision. A question like "how much did our top account spend last quarter?" is a precise query, not a document search. The answer is a number, computed from rows of data against specific criteria.

• Reusable data models. The customer entity, the order entity, the ticket entity — these should be modeled once and reused across every agent and workflow that needs them, rather than rebuilt from scratch by every team.

• Governance by default. Whatever an agent can reach still has to respect enterprise access rules — folder-level permissions, role-based controls, entity-level policies, data lineage, all of it. Without that, every new agent becomes a new compliance surface that has to be managed from zero.

Not Another Data Warehouse — An Operational Context Layer

Here's the interesting part: UiPath is explicit that Data Fabric, their product for solving this, is neither an ETL platform nor another data warehouse. Most enterprises already run Snowflake, Databricks, or BigQuery for analytics. What they're missing isn't more storage — it's a layer that reaches data where it already lives, models it as connected business entities, and governs access consistently across the board.

Two data patterns sit underneath it. Native entities store data directly inside Data Fabric, which works well for anything created or managed by agents themselves. Federated entities stay connected to external systems while the underlying data remains at its source, so there's no need to copy and duplicate records over and over.

What This Looks Like in Practice

A capability currently in preview, called structured context, lets an agent bind directly to one or more Data Fabric entities. When a user asks a question, the agent reads the available entity descriptions, decides which one (or combination) is relevant, translates the natural-language question into a specific query, runs it against the live data source, and builds its answer from the results.

In practice, this plays out in two ways. A conversational agent used by a sales analyst can answer "is order ABCD eligible for a warranty replacement?" by checking order, customer, and item entities connected to both Oracle and SQL Server — and return a decision in seconds. An autonomous lead-assignment agent, meanwhile, can pull account data from Salesforce, check whether that account's annual spend crosses a given threshold, and automatically route the lead to a dedicated account manager or into the shared pool.

Why This Matters If You're Building an Enterprise AI Strategy

The agents your organization runs next year will likely look nothing like the ones running today — models will change, frameworks will change, and so will the way agents get built. What stays constant is the data itself, and the entities that describe it. Investing in a connected, governed data layer now means every agent built from here forward has something reliable to stand on, instead of being rebuilt from scratch each time a new one comes along.

Building a trustworthy data foundation for AI agents isn't just about picking the right tools — it's also about knowing exactly who can access what, and how that risk gets managed. UiPath Data Fabric is built for exactly this: giving agents live, governed access to the entities that describe how your business actually runs, instead of leaving them stuck with whatever's in a document. Explore Data Fabric to see how it fits into your AI strategy.

Author: Ghea Devita

Marketing Communication PT Perkom Indah Murni