Frequently Asked Questions Agentic Data Management (ADM)

What is Agentic Data Management (ADM)?

Agentic Data Management (ADM) is an AI-powered, autonomous approach to data management that detects, understands, and resolves data challenges in real time. 

It is a complete rethink of Data management for the AI age, It replaces the traditional data management stack of data catalogs , quality , governance and observability tooling and replaces it with an AI powered platform which will revolutionize the space

It goes beyond traditional observability by leveraging AI agents, contextual memory, and automated actions to ensure data is always reliable, governed, and optimized across an enterprise.

How is ADM different from traditional Data Quality (DQ) and Data Observability tools?

Traditional Data Quality (DQ) solutions rely on predefined rules and batch processing, making them rigid and reactive. Data Observability provides continuous monitoring but still requires human intervention to resolve issues. ADM takes the next step by:

• Using AI to understand context (not just detect anomalies but explain their impact).
• Taking autonomous actions to fix data issues in real-time by leveraging ADOC hyperscale processing
• Providing memory & reasoning, so it can recall past decisions and apply learnings.

How does ADM use AI and LLMs?

ADM leverages Large Language Models (LLMs) and AI agents to:

• Interpret complex data environments and detect anomalies.
• Recall past resolutions and suggest optimal fixes based on history.
• Automate governance, security, and compliance by reasoning across datasets.

What are the core components of ADM?

ADM consists of:

1. AI Agents → AI agents that analyze context and take precise actions.
2. xLake Reasoning Engine → A scalable processing engine with built-in governance and security.
3. Business Notebook → A collaborative, natural-language interface with interactive analytics.
4. Agent Studio → Allows enterprises to build and deploy their own AI-powered agents.

How does ADM improve data governance and compliance?

ADM automates policy enforcement, anomaly detection, and remediation by:

• Applying AI-based rules to detect non-compliance before it impacts business.
• Maintaining an audit trail for all data corrections and actions.
• Ensuring secure, governed data pipelines across hybrid and multi-cloud environments.

Can ADM scale to handle petabytes or exabytes of data?

Yes. ADM is proven at exabyte scale, running across hyperscalers, data lakes, and on-prem environments. It dynamically optimizes workloads and integrates with modern architectures, ensuring enterprises can manage data growth without performance bottlenecks.

How does ADM impact cloud costs and performance?

ADM optimizes data pipelines, storage, and compute resources using AI. It:

• Identifies inefficiencies in data workflows.
• Reduces unnecessary cloud spend by governing data movement.
• Optimizes workloads in real time, improving performance and reducing operational overhead.

How does ADM integrate with existing data ecosystems?

ADM integrates with data lakes, warehouses, cloud platforms, and governance tools via:

• APIs and native connectors (AWS, GCP, Azure, Databricks, Snowflake, etc.).
• Streaming and batch processing support (Kafka, Spark, Kubernetes).
• Existing observability and monitoring solutions to enhance data visibility.

What makes ADM relevant in today’s AI-driven enterprise landscape?

With AI becoming the backbone of enterprise decision-making, businesses need trusted, high-quality data at scale. ADM ensures that:

• Data is AI-ready – cleansed, structured, and contextually accurate.
• AI operations are explainable – ensuring regulatory compliance and trust.
• Data pipelines self-heal – reducing manual intervention and operational risk.