Since its inception, agentic AI has felt like a distant aspiration. Today, agents are here, and enterprise adoption is accelerating. Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025.
Agents arrive with incredible, broad intelligence, but lack the knowledge of your operating model: your customers, policies, approvals, exceptions, business rules, systems, and operational history.
That context gap becomes more consequential as agents move from answering questions to acting inside core business processes. An agent that summarizes information needs access to knowledge. An agent that drafts claims, reconciles invoices, and triages service requests needs controlled access to live operational data, workflows, and audit trails.
For many organizations, the fastest path to value is to connect each agent directly to the systems it needs: CRM, ERP, case management, document repositories, compliance databases, and more.
The more agents an organization deploys, the more important it becomes to give them consistent, governed access to enterprise context.
That approach may work for the first few agents. But as agents multiply, those direct connections create a fragile operating environment where access logic is duplicated, audit trails are scattered, and every system change becomes harder to manage. This cycle is the primary driver of agent sprawl.
The cost of agent sprawl
Organizations are in the pursuit of meeting AI agent mandates to introduce system-wide agent integrations. Teams want the fastest path to value, so they deploy the agent where the data lives: in your systems of record like your CRM, ERP, and ITSM.
The result is your agents inherit the same sprawl and silos that affect your enterprise data. Every system of record accumulates a growing web of agent connections, creating an unsustainable operational environment where:
Governance fragments. Access rules are reimplemented for every agent, meaning they inevitably drift and decay over time.
Audit becomes impossible. When an audit must determine what data an agent accessed, the answer is scattered across dozens of disconnected logs—or missing entirely if it was never captured in the first place.
Change gets expensive. A single schema change in a source system ripples through every connected agent, significantly increasing the impact of any update.
70% of enterprise leaders report difficulty integrating AI agents in systems, noting that their AI agents lack access to the right data.1
1 Harvard Business Review Study, What Drives AI Value, 2026
The limits of conventional solutions
When faced with the need to give agents context, many organizations often reach for one of three approaches:
A refined LLM that has enterprise data baked in, but suffers from upkeep costs and risks of data staleness, especially for live operations.
A heavy data modeling effort that may create a clean mesh of enterprise data, but can be rigid, expensive, and slow to adapt as use cases change.
A read-only knowledge layer to help an agent answer questions, but falls short when agents need to update a case, route an exception, or trigger the next step in a workflow.
Real-time operations need real-time solutions that support both insights and actions. A claims agent does not just need to read policy terms. It needs to update the claim, route exceptions, and capture a record of what happened. If the agent cannot change data in a controlled and reliable manner, the efficacy of that agent collapses.
Agents need more than a knowledge layer. They need a secure, governed solution that lets them understand and act on enterprise data safely.
Data fabric as your operational context layer
The solution to agent sprawl is an operational context layer built around three key requirements for agentic work:
Unified context. Agents need a unified view of operational data across CRM, ERP, case management, document repositories, and other systems. Appian data fabric turns fragmented data into business-friendly context, so agents can reason over customers, policies, cases, assets, and approvals in the language of the business.
Strict governance. Agents should not carry their own disconnected access logic. Security rules should be defined once at the data layer, so every agent inherits the right row-level and field-level permissions based on the user, role, and process context.
Transactional power. Enterprise agents need to update records, route exceptions, and trigger workflows. Appian data fabric gives agents performant, secure access to live operational data, granting them controlled access to change data across systems of record. This ensures agents can be active participants in any operation.
Making your data fabric agent-ready
Solving agent sprawl is about making enterprise data usable by agents in a safe, consistent way. But how does a data fabric make this possible?
It starts with semantics. Agents read metadata much like they read prompts, so field names and descriptions should be written for comprehension, not just technical accuracy. A database field like “cust_nm_prim” may be clear to a developer, but an agent needs to understand that it represents the customer’s primary legal name for billing and contract correspondence.
Relationships matter, too. Naming relationships with active verbs helps agents understand how records relate to one another. “Customer owns case” gives an agent more reasoning context than “customer_case_link.”
Finally, preparing for agents means preparing for audits. Agents inherit their permissions from initiating users, so it is important to use platform audit trails to see exactly what data agents are accessing under what user context. This ensures that every time an agent retrieves data or takes an action, it leaves behind a clear audit trail.
The teams that win the next phase of enterprise AI will not be the ones with the most agents. They will be the ones whose agents have the right context.
Watch a demo to see how Appian data fabric eliminates agent sprawl, unites your data and processes, and gives your AI agents the real-time context they need to execute work safely.
