Data Intelligence for AI Agents

How data intelligence strengthens AI agents

AI agents rely on accurate, governed, high-quality, and context-rich data to perform tasks, reason, make decisions, and interact autonomously. Data intelligence provides the MCP servers, metadata, lineage, observability, governance, catalog context, and semantic understanding needed to ensure AI agents operate with trusted and explainable information.

AI agents cannot function reliably without a strong data intelligence foundation. They require complete, up-to-date knowledge of data definitions, dependencies, relationships, quality, and governance rules to act safely and effectively.

What are AI agents?

AI agents are autonomous or semi-autonomous systems that:

• Perform tasks.
• Retrieve and analyze data.
• Execute workflows.
• Interact with applications.
• Make recommendations or decisions.
• Learn from user interactions.

Examples include:

• Customer support agents.
• AI-powered analytics assistants.
• IT automation agents.
• Fraud detection agents.
• Supply chain optimization agents.
• AI copilots for business applications.

To operate effectively, AI agents must understand data meaning, trustworthiness, lineage, governance policies, and reliability. 

Why AI agents require data intelligence

Prevents hallucinated responses

Without accurate metadata, lineage, trust signals, and semantic grounding, agents risk generating incorrect or misleading outputs.

Supports explainability

Agents must provide clear reasoning paths for decisions, especially in regulated industries.

Ensures access to governed data

Agents must only use approved, policy-compliant datasets.

Enables domain-specific understanding

Semantic definitions and glossary terms help agents understand business language and context.

Reduces operational risk

Quality and observability signals ensure agents rely on trustworthy, up-to-date data.

How data intelligence improves AI agent performance

Provides metadata and semantic grounding

Data intelligence offers rich metadata that informs agent reasoning, including:

• Definitions.
• Synonyms.
• Domain terms.
• Relationships.
• Classifications.

Ensures agents use trusted data

Trust indicators include:

• Freshness score.
• Quality score.
• Drift status.
• Completeness.
• Lineage depth.

Agents can evaluate trust signals before acting.

Provides lineage for explainability

Lineage allows agents to answer:

• Where data came from.
• How it was transformed.
• Which systems were involved.
• How reliable or compliant the data is.

Enforces governance rules

Agents must respect:

• Privacy constraints.
• Role-based access.
• Regional restrictions.
• Sensitivity labels.
• Domain-specific rules.

Data intelligence enforces policies automatically.

Improves decision-making with real-time observability

Agents use observability signals to detect:

• Anomalies.
• Drift.
• Stale data.
• Unexpected trends.

This prevents agents from using unreliable data.

Integrates catalog context

Agents can use the data catalog to:

• Find the correct dataset.
• Compare multiple datasets.
• Understand definitions.
• Validate usage context.

Powers agent reasoning through knowledge graphs

Knowledge graphs give agents a structured semantic backbone to understand:

• Relationships.
• Hierarchies.
• Concepts.
• Entities.

Architectural elements supporting AI agents

Metadata and semantic layer

Informs the agent’s understanding of entities, fields, and business meaning.

Lineage and impact graph

Provides traceability and supports explainable reasoning.

Observability and quality signals

Ensure inputs are timely and trustworthy.

Governance engine

Applies approved access, masking, and compliance rules.

Domain catalog

Allows agents to retrieve data in a policy-aware way.

Knowledge graph integration

Supports context-rich reasoning, RAG augmentation, and entity linking.

Use cases for data intelligence-enhanced AI agents

Analytics copilots

Agents assist with:

• Query generation.
• Dashboard explanation.
• Metric clarification.

Customer service automation

Agents need governed access to customer profiles and interactions.

Finance and compliance

Agents evaluate calculations using trusted lineage and quality signals.

IT automation agents

Agents troubleshoot issues using metadata, lineage, and observability.

Data engineering agents

Agents can validate pipelines using quality and drift metrics.

Operational decision agents

Agents support:

• Supply chain.
• Logistics.
• Inventory planning.
• Fraud response.
• Personalized recommendations.

Responsible AI enforcement

Agents use governance policies to ensure safe and compliant model behavior.

Why organizations choose Actian for AI agent enablement

Actian Data Intelligence Platform provides AI agents with:

• MCP servers for LLMs
• Unified metadata across hybrid and multi-cloud systems.
• Lineage for explainable decisions.
• Policy-aware access and governance.
• Trust signals embedded into workflows.
• Knowledge graph integration.
• Quality and observability data.
• Semantic understanding and business glossary context.
• Ready-to-use governed data products and contracts.

Actian ensures AI agents operate safely, responsibly, and effectively using trusted, explainable, governed data.