AI agents don't just need data. They need to understand the world they're operating in — what your data means, how your organisation works, what decisions it supports, and who's responsible for what. The Context Layer is that understanding.
Ask an AI agent:
"Which customers churned last quarter?"
It looks like a simple query. But to answer it correctly — not plausibly, correctly — the agent needs four layers of context.
| What the agent needs | Why it doesn't have it |
|---|---|
| Data context. Which tables and columns? |
47 tables with "customer" in the name across Snowflake, Databricks, and the data lake. Which are canonical? Which are stale? Which have the right granularity? |
| Meaning context. What does "churned" mean? |
No renewal in 90 days? No login in 30 days? Explicit cancellation? Three teams define it three different ways. |
| Knowledge context. What does the team know that isn't written down? |
Last quarter, the product team ran a free extension for enterprise accounts. Those look churned in the data — but they're not. The CS team knows. The data doesn't. |
| User context. Who's asking? |
The CFO wants revenue impact. Product wants feature attribution. Same question, two correct answers — depending on who's asking. |
Without a Context Layer, the agent guesses. With one, it understands.
This is the problem AI researchers call the world model gap: an agent acting in a complex environment without an internal representation of how that environment actually works. Language models predict the next word. World models predict the next state of reality. Your AI agents need a world model of your organisation.
A world model starts with ground truth: what exists in the environment. The Enterprise Data Graph connects 80+ data sources — your catalogs, governance policies, lineage paths, quality scores — into one living graph. Baseline AI enrichment runs automatically. This is the state layer of the organisational world model.
Explore the Enterprise Data Graph →A world model doesn't just know what exists — it knows what things mean. Context Studio is where humans and AI build that meaning together. AI bootstraps what it can from the data graph. Humans bring what the data can't capture — the business logic, the edge cases, the context that only exists in someone's head. The result is meaning both can act on.
See Context Studio →A world model gets smarter with use. But it needs someone paying attention. Every agent interaction leaves a trace. When an answer is wrong — when the agent picks the wrong table, misapplies a definition, or misses an edge case — the trace points back to the context gap that caused it. Humans review those traces. They close the gaps: correcting definitions, updating business logic, adding the context that only exists in someone's head.
Learn about Context in Production →Every world model needs a memory architecture. For your organisation, that's the Context Lakehouse — one Iceberg-native store for all enterprise context. Semantic layers, context graphs, and ontologies in a single queryable store. Any AI agent accesses it via MCP, SQL, or APIs. Any cloud. Any framework.
"Your organisation's understanding of itself. All of it."
Built natively on Apache Iceberg — the same open format your data lakehouse already runs on. No proprietary formats. No walled gardens. Your context travels with you.
The concept of the organisational world model is grounded in three decades of AI research — not in marketing language.
The path to general AI goes through world models and planning.
Their world models are for robots, games, and autonomous vehicles. The organisational world model is the enterprise instantiation — the same structural architecture, applied to the data, meaning, knowledge, and governance that make up how a business actually works. In a world where intelligence is commoditised, what makes two organisations different? Their model of how they work. For enterprise AI, that world model is the context layer.
When AI works on the most consequential decisions in the world — a diagnosis, a discovery, a deal — it deserves to know the full truth. What your data means, what your organisation knows, what it can trust. All of it.
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