Governed Access
The agent must only retrieve data it is allowed to use, with policies enforced at the right layer.
Open Data Infrastructure
Open Data Infrastructure for AI Agents
Agentic systems need more than model access. They need governed, explainable, interoperable access to trusted data context.
AI agents turn data infrastructure from a back-office analytics concern into a runtime dependency. When software can plan, query, summarize, trigger workflows, and make recommendations, the quality of the data interface becomes part of the product experience.
Why Agents Change the Data Problem
Traditional analytics workflows assume a person can interpret missing context, ask a data engineer for help, or wait for a dashboard refresh. Agents do not have that luxury. They need machine-readable context, permission-aware retrieval, lineage, and clear failure states.
If the data estate is fragmented across closed APIs, proprietary warehouses, undocumented extracts, and spreadsheet workflows, the agent inherits that fragility.
The ODI Requirements for Agents
Metadata Awareness
The agent needs schemas, ownership, freshness, descriptions, quality signals, and semantic context.
Interoperable Interfaces
Tools should be able to query, inspect, and move data through open standards rather than fragile one-off integrations.
Explainability
Every answer should be traceable back to the data, transformations, policies, and assumptions behind it.
A Practical Reference Architecture
- Source systems expose reliable operational data access through APIs, CDC, or export mechanisms.
- Data lands in open formats with table-level metadata and transaction history.
- A catalog coordinates identity, permissions, schemas, policies, and table discovery.
- Query engines and semantic services provide controlled retrieval paths.
- Agents use governed tools that can retrieve both data and context, not just raw rows.
Common Failure Modes
Teams often start with a model and a demo, then discover that production depends on data contracts they do not have. The common failure modes are stale extracts, ambiguous metrics, missing lineage, over-permissioned agents, hidden vendor lock-in, and no reliable way to explain where an answer came from.
ODI is the antidote: make the data layer open, inspectable, governed, and portable enough that the AI layer can be trusted.