What Is Agentic Data?

Dashboards tolerate ambiguity. Agents do not. If the system is going to act, the data contract has to get sharper.

A definition that is useful

Agentic data is data packaged with the metadata and controls required for an automated system to use it safely. It is not a new storage format. It is a discipline: you treat permissions, provenance, and action traces as first-class parts of the dataset.

Why agentic systems change the data contract

Traditional analytics assumes a human in the loop. If a metric looks wrong, a human questions it. If access looks suspicious, a human reports it. If the query is ambiguous, a human clarifies intent.

Agents collapse that buffer. An agent can query, decide, and act in one loop. That makes three things non-negotiable:

• permissions: the agent must only see what it is allowed to see
• provenance: the agent must be able to retrieve source, freshness, and lineage signals with the data
• auditability: humans must be able to reconstruct why the agent got a piece of data and what it did with it

This is why ODI treats governance as infrastructure behavior, not as a process document.

Properties of agentic data

Agentic data usually has these properties:

• machine-actionable metadata: owners, domains, and meanings that a system can use programmatically
• policy-carrying access: authorization that is evaluated in the data path and the tool layer
• retrieval with provenance: results include lineage, freshness, and quality signals
• action traces: every agent action is logged with inputs, retrieved context, and outputs
• contract-driven datasets: schemas and identifiers treated as stable interfaces, not suggestions

If you have a semantic layer today, this is the next step. See From Semantic Layer to Context Graph.

Where it fits in the ODI stack

Agentic data sits at the intersection of:

• open tables and catalogs: durable storage and interoperability contracts
• governance: consistent enforcement and audit
• context layer: retrieval that is governed and explainable

If your agent stack can bypass the data platform controls, you do not have agentic data. You have a security incident waiting for a prompt.

For the ODI view, start with ODI for AI Agents and ODI Glossary.

Common anti-patterns

• RAG without governance: retrieval that returns text and facts without policy or provenance
• context without freshness: cached answers that nobody can date or validate
• permissions checked only in the app: access control that disappears when a tool connects directly to data
• no action traces: automation without reconstructable audit logs

Those anti-patterns are why AI makes ODI more important, not less.

A checklist for teams building agents

• Define the domains and datasets agents are allowed to access.
• Enforce access controls in the data path, not only in the UI.
• Attach provenance (freshness, lineage, owner) to retrieved context.
• Log agent actions with inputs, retrieved context, and outputs.
• Build human review and rollback for high-risk actions.

Agents become safe when audit becomes normal.

Sources to start with

Start with governance and provenance standards, then map them into your architecture.

• OpenLineage documentation
• NIST AI Risk Management Framework
• Apache Iceberg table specification