AGENTIC DATA FOUNDATION
Data foundations for enterprise AI agents
An agentic data foundation gives your agents shared definitions, grounded retrieval and permission-aware access. We build those layers on your existing data estate.
- A semantic layer, so meaning is explicit and defined once
- Retrieval infrastructure that grounds answers in your facts
- Permission-aware access, so autonomy never bypasses controls
Most data estates are missing at least half of it.
AI-ready is a specific set of capabilities agents depend on, not a vague aspiration.
Get these right and agents become reliable. Get them wrong and every agent inherits your data's worst habits, at machine speed. The foundation builds on [data management and governance](/data-management-and-governance) for trust and an [open data lakehouse](/open-data-lakehouse) for the substrate.
The semantic and retrieval layers are what turn a data platform into an AI foundation. The access and observability layers are what make it safe to let agents act, not just read.
A semantic layer
So an agent knows "active customer" means one thing, defined once, not five conflicting versions. An agent cannot infer your business logic.
Retrieval infrastructure
Vector stores and search over your documents and data, so agents ground answers in your facts. Grounded in evidence, not guessing.
Governed access
Permission-aware, so an agent sees exactly what its user is allowed to see, nothing more. Autonomy without access control is a breach.
Lineage and quality
So every answer or action traces to trusted source data, and can be audited after the fact. Every action is traceable and reviewable.
Before you scale agents, build the foundation they stand on.
What your agentic data foundation includes.
- A semantic layer defining shared entities, metrics, and definitions for agents to reason over.
- Retrieval infrastructure: vector stores, embeddings, and hybrid search over your data and documents.
- Permission-aware access, so agents inherit user entitlements, enforced and audited.
- Lineage and quality, so every agent action traces to trusted source data.
- Agent-facing interfaces: governed APIs and tools for agents to query and act safely.
Five steps, from an AI-readiness gap to agents you can trust.
Assess
Map AI-readiness: definitions, retrieval gaps, access risk, and quality across priority domains.
Establish
Stand up the semantic layer and a governed source of record agents can reason over.
Build
Create retrieval infrastructure and embeddings over the data agents will actually use.
Govern
Make autonomy permission-aware and fully auditable, so access is never bypassed.
Instrument
Add observability, so what agents retrieve and do is monitored and evaluated over time.
Six layers, from governed data to observable agents.
A representative architecture by layer. Retrieval or fine-tuning, the foundation supports both, but they solve different problems.
For most enterprise agents, retrieval on a governed foundation is the workhorse, with fine-tuning reserved for behavior. This foundation is the data prerequisite for [scaled GenAI and AI platforms](/scaled-genai-and-ai-platforms) and the wider [Enterprise AI](/enterprise-ai-services) practice.
What the foundation builds on, and what it enables.
Open Data Lakehouse
The open, governed substrate the foundation stands on.
Data Platforms
The platform services that run and scale the foundation across teams.
Data Management and Governance
The catalog, lineage, and quality the foundation depends on for trust.
Scaled GenAI and AI Platforms
The AI platform practice this foundation feeds and grounds.
Enterprise AI
The agents and applications that depend on AI-ready data.
Infrastructure and Platforms
The parent category this foundation practice belongs to.
What data and AI leaders ask us first.
Because ungoverned, ambiguous data produces ungrounded answers at scale. The foundation gives agents shared meaning, safe retrieval, and permission-aware access, which is the difference between a reliable agent and a confident wrong one.
For retrieval-grounded agents, yes. For agents acting on structured, well-defined data, the semantic layer and governed APIs may matter more. We build what your use cases actually require.
Access is permission-aware: an agent inherits the entitlements of its user, enforced at the data layer and fully audited. Autonomy never bypasses your access controls.
Build the governed, AI-ready foundation your agents need before you scale them.
A semantic layer, retrieval, and permission-aware access, so agents act on data they can find and trust.