Utilizing AI Agents with Quest and Moveworks

The next wave of innovation is being driven by agentic AI, where intelligent agents can reason, adapt, and act autonomously—supported by self-optimizing data architectures.

From automated data discovery and quality enforcement to policy-driven governance and dynamic pipelines, autonomous data management is moving from concept to practice.

Forward-thinking enterprises are already using these technologies to improve efficiency, accuracy, and decision-making at scale.

Enterprise AI World recently held a webinar, The Rise of Autonomous Data: How Agentic AI Is Redefining Data Management, with Yetkin Ozkucur, global sales engineering director at Quest and Christie Fitzgerald, senior manager, presales at Moveworks, who discussed how agentic AI is transforming data management across industries.

Ozkucur presented an example of the executive of the future who ensures data is trusted, pipelines are resilient, and semantics are consistent so that humans and AI agents can work together with confidence.

A week in the life of this executive includes working heavily with AI agents as she builds trusted data for AI and ensures consistent meaning throughout the enterprise.

However, in the present day, policies are static documents and depend on humans reading and applying to systems. Reviews are conducted quarterly and violations go undetected until next audit. AI teams spend most of their finding the right data. Data poorly documented, missing metadata often inconsistent.

Active metadata is a continuous, real-time intelligence layer that captures technical, business, operational, and social metadata from all systems.

It is the connective tissue that links every component of the modern data estate. Converged data management platforms leverage this shared metadata to automate and federate data management activities.

It transforms metadata from a passive, after-the-fact record into an active driver for orchestration and control.

It enables:

• Automated governance and compliance
• Data product automation
• Intelligent cost optimization (FinOps)
• Augmented data management

AI agents are the workforce’s multiplier, Ozkucur explained. The challenge right now is that human effort can’t keep pace with the scale of modern data. The solution is AI agents move from assisting humans (copilots) to autonomously planning and executing tasks within policy guardrails.

Autonomous AI without accountability is a massive security and compliance risk. The solution is an evolution of IAM that provides identity, authentication, and authorization for both human and AI agents, he said.

There are 5 shifts driving autonomous data operations, Fitzgerald noted. This includes:

• Manual Discovery to Automated Discovery
• Reactive Quality Management to Continuous Quality Enforcement
• Static Governance to Policy-Driven Dynamic Governance
• Rigid Pipelines to Adaptive Pipelines
• Human as Bottleneck to Human as Oversight Layer

Metadata plays an important role in this. If data is the fuel for agentic AI, metadata is the engine management system, she said.

The Moveworks approach includes agents that reason across metadata for safe, contextually appropriate decisions without moving or duplicating data. When an employee asks about benefits, the agent finds the right policy for their role, location, and employment status before surfacing anything to the requestor.

She recommended starting with a workflow, not a data project. Pick one high-volume, painful area and start executing. You'll learn more in 30 days than 6 months of planning, she said.

Let AI agents surface your data problems. AI can show you immediately where knowledge is broken and where employees fall through cracks.

And govern as you go, not before you go, she said. Ensure role-based access, compliance controls, and admin oversight are built in from day one.

For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.