Making AI agents work in the enterprise

AI agents are no longer science projects. Across industries, CIOs and data leaders are exploring how they can reduce manual effort, improve decision-making, and move governance out of the backlog. But most initiatives stumble at the same place: data.

Agents, whether custom-built or bought, are only as good as the information they act on. Too often, data is siloed, inconsistent, or outdated. Without a governed, real-time connection to trusted data, automation becomes fragile at best and risky at worst.

This is the problem Reltio set out to solve with AgentFlow, a platform built to connect AI agents with the trusted, governed enterprise data they need to deliver real outcomes.

The gap between AI ambition and reality

Many enterprises have strong ambitions for AI, but progress slows once teams move from pilots to production. Business leaders expect quick returns, while data stewards struggle with quality issues and governance bottlenecks. CIOs are left in the middle, balancing innovation with operational risk.

The pattern is familiar:

• Data governance backlogs delay projects.
• Poor-quality records increase compliance risk.
• Disconnected systems create friction for business users.

Generic copilots or DIY agents often add more complexity instead of solving these problems. They don’t understand the enterprise context, and they can’t enforce governance or security at scale.

What changes with Reltio AgentFlow

Reltio AgentFlow was designed with these realities in mind. At its core, it connects trusted, unified data from the Reltio Data Cloud with AI agents through a governed execution layer called the AgentFlow MCP Server. On top of that foundation, enterprises can deploy a growing set of Reltio AgentFlow prebuilt agents or integrate their own. Prebuilt data governance agents, for example, automate routine tasks across data and business workflows—eliminating backlog, reducing human effort, and allowing teams to focus on high-impact priorities.

Here’s what that means in practice:

• Governed access to data: Every agent action inherits enterprise policies, role-based access, and audit controls. CIOs don’t have to trade speed for compliance.
• Real-time data foundation: Agents act on continuously updated, context-rich data rather than static snapshots.
• Purpose-built agents: Tasks like entity resolution, data validation, and quality remediation can be automated safely and repeatably.
• Flexibility: Enterprises can use Reltio’s prebuilt agents, bring their own, or work with third-party agents. All gain secure access through the Reltio AgentFlow MCP Server.

Early lessons from the field

Some organizations are already applying Reltio AgentFlow in production-like settings. Radisson Hotel Group and Eaton Corporation, for example, are piloting agents to resolve matches, manage hierarchies, and improve data quality at scale.

Partners such as Cognizant, ZS, and Tata Consultancy Services (TCS) are also collaborating with customers to integrate Reltio AgentFlow into their business operations. Their focus is not on experimentation but on solving recurring challenges—compliance tasks, governance bottlenecks, and process inefficiencies—that drain productivity.

What it means for data leaders

For CIOs and data executives, the message is straightforward: if your AI projects are stalling, the issue is likely not the agent itself but the lack of context and governance behind it. Without trusted, real-time data, agents can’t be relied on to make decisions or automate workflows at scale.

Reltio AgentFlow is one approach to closing this gap. By combining data unification, governed access, and ready-to-use agents, it offers a path to confidently move beyond experimentation and into measurable impact.