Ground AI agents with live enterprise context using UiPath Data Fabric

The smartest AI agent in your enterprise can't tell you whether your largest customer paid their last invoice, or which support tickets are overdue in a specific region.

Not because the agent lacks intelligence, but because its grounding is only as complete as the enterprise data it can securely access.

The grounding wall

Many enterprise AI deployments begin with unstructured knowledge: PDFs, knowledge articles, wiki pages, and files in shared repositories. In a document-grounding pattern, retrieval finds relevant passages at query time, adds them to the model’s context, and helps it answer questions such as, “What does our return policy say?”

But that pattern alone is not enough to answer, “Which support tickets are overdue in the West region?” Many of the questions that keep a business running—the ones sales representatives, claims adjusters, and accounts payable analysts ask throughout the day—depend on live operational records and the relationships between them, not just documents. The answers sit across client relationship management (CRMs), enterprise resource planning (ERPs), ticketing platforms, data warehouses, and operational databases, where information changes continuously and access must remain governed.

A document-only grounding layer cannot provide that complete, current view.

That is the wall enterprise AI often hits. UiPath Data Fabric is designed to break through it, giving agents, apps, and automations a unified, governed way to work with current data across supported enterprise systems.

What grounding on real business data actually requires

Copying every operational record into a vector store is not the answer. Vector search is useful for finding semantically similar information, but many business questions require exact filters, joins, calculations, and shared business definitions.

Grounding agents in the data that runs the business requires four capabilities that document-centric AI architectures were not designed to provide on their own.

• Live or near-real-time access: the agent has to see the data as it is right now, not as it was at last night's extract, transform, and load (ETL) run. Most of these answers change by the hour, not the week.

• Structured precision: "How much did our top account spend last quarter?" is a precise query, not a document lookup. The answer is a number computed from rows, scoped by criteria.

• Reusable models: the customer entity, the order entity, the ticket entity should be modeled once and consumed by every agent, app, and workflow that needs them—not rebuilt by every team that touches them.

• Governance by default: whatever the agent reaches has to obey enterprise access rules—folder-level permissions, role-based controls, entity-level policies, lineage. Otherwise, every new agent becomes a new compliance surface.

Live, structured, reusable, governed. That's what we mean when we say "agent-ready data."

An operational context layer, not another data warehouse

Most enterprises already run Snowflake, Databricks, or BigQuery for analytics—they don't need another data warehouse. They need a layer that reaches the data where it lives, models it as connected business entities, and governs access consistently.

That's the role Data Fabric plays. It isn't an ETL platform. It isn't a data warehouse. It's an operational context layer that sits on top of the systems where business data already lives—CRM, ERP, databases, SaaS apps—and turns that data into reusable, queryable business entities that agents and workflows consume directly.

Two data patterns underpin the model.

• Native entities persist records in Data Fabric. They are well suited to store state and business data created, enriched, or managed by UiPath agents, apps, and automations.

• Federated entities connect to supported external systems while the underlying data remains at the source. Data Fabric retrieves the live data when it is needed, without creating and maintaining a separate replicated copy.

These patterns can also be combined within a unified entity, bringing local and external fields together around a shared business object.

Teams don't have to model entities by hand. With coding agent-enabled modeling, the coding agent of your choice can analyze schemas, infer relationships, and build entity models automatically. Visual tools remain for teams that prefer a no-code path.

Structured context: agents querying live business data

This thinking shows up most concretely in a capability now in preview: structured context in Data Fabric.

Structured context lets an agent bind to one or more Data Fabric entities directly. When a user asks the agent a question, the agent reads the entity descriptions, decides which entity (or combination) to query, translates the natural-language question into an entity-specific query, runs it against the live source, and reasons over the returned rows.

In practice, this shows up in two patterns:

• A conversational agent used by a sales analyst answers "Is sales order ABCD eligible for warranty replacement?" by querying order, customer, and item entities federated to Oracle and SQL Server—and returns a decision in seconds.

• An autonomous seller-assignment agent pulls account data from Salesforce, checks whether the account's annual spend clears a certain threshold, and routes the lead to a dedicated account manager or the shared pool accordingly.

It's the same pattern in every case: live data, structured queries, reusable entities—with full trace visibility and full enterprise governance over what the agent sees.

Structured context also replaces an older pattern. Teams who grounded agents with CSV-backed structured query indexes can now bind the same data as a live entity. No more snapshot drift. No more re-uploading every week. The migration path is straightforward, and the upside—live data, richer metadata, entity-level guardrails—is significant.

A foundation for what's next

The agents your business runs in 2027 won't be the ones you're building today. Connectors, frameworks, and models will all change. What stays constant is the data—and the entities that describe it.

Investing in a connected, governed, agent-ready data layer now means every agent you build from here forward has somewhere reliable to stand. The customer entity, the order entity, the ticket entity, the policyholder entity—model them once, govern them once, consume them everywhere.

Data Fabric is on a path toward a knowledge graph that gives agents semantic understanding of how entities relate—not just what data exists, but how it connects. That's the next chapter, and it starts with the layer being built today.

Get started

When your agents can reason on real business data, they can do real, meaningful, and impactful work. Data Fabric brings the enterprise context your agents need, modeled as entities your teams can reuse, under the governance your enterprise requires. Structured context. Connected, actionable intelligence. Agent-ready data. Explore Data Fabric to see it in action.