Open Data Infrastructure
Agentic Data Product Design
How data products change for agents across contracts, examples, permissions, evaluation hooks, and explainable failure modes.
Human data consumers can ask clarifying questions. Agents usually turn missing context into wrong behavior.
The practical problem
Traditional data products are built for analysts, dashboards, applications, and operational teams. Agentic systems add a new consumer: software that can read, reason, call tools, and sometimes take action.
That changes the product design. The data product has to expose contracts, examples, permissions, lineage, quality signals, failure modes, and evaluation hooks in a way machines can inspect. Documentation written only for humans is not enough.
Core idea: an agentic data product packages data with the operating context an agent needs to use it safely.
What changes in the product
The contract needs to be explicit. Agents should know the grain, allowed filters, sensitive fields, update cadence, owner, and intended use. Ambiguous data products create ambiguous actions.
Examples become infrastructure. Good and bad examples help agents understand how the data product should be used, how it should not be used, and which failure modes matter.
Permissions need to account for delegation. An agent acting for a manager, support representative, or customer-facing workflow may need narrower permissions than the underlying service account could technically reach.
What breaks first
- The agent reads the right table but misunderstands the grain.
- The data product has a human owner but no machine-readable use constraints.
- Evaluation examples do not cover denied access, stale context, or ambiguous entities.
- The product exposes data but not the failure signals an agent needs before acting.
Questions to ask
Use these questions before exposing a data product to agents.
- What is the product grain, and can the agent inspect it?
- Which uses are allowed, restricted, or explicitly forbidden?
- Which examples teach correct and incorrect use?
- Which evaluation hooks prove safe behavior after changes?
- Can lineage and policy explain every agent answer or action?
For related ODI patterns, read What Is Agentic Data?, AI Data Contracts for Agents, and Why Enterprise Agents Fail Without Data Governance.
Sources to start with
Agentic data product design pulls from AI risk, evaluation, lineage, provenance, and policy practice.