What Is a Context Graph?

A context graph is a connected model of the business context surrounding an organization's data - the concepts, definitions, processes, people, and data assets, and the relationships between them - assembled so that an AI agent (or a person) can traverse it to understand a domain and reason about it correctly. Where a raw dataset answers "what are the values," a context graph answers the harder questions an AI must resolve first: "what does this concept mean, what relates to it, where does the data behind it come from, and how is it governed?"

It matters because AI agents fail not on facts but on context. An agent asked about "customer churn" needs to know what churn means in this business, which data measures it, how that data connects to customers, orders, and contracts, who owns it, and whether it can be trusted - a web of context a human domain expert holds intuitively. A context graph makes that web explicit and traversable, so an agent can assemble the same grounding the expert would. It is the relationship-rich substance behind a context layer, and a close cousin of the metadata knowledge graph - focused less on cataloging the data estate and more on modelling the business meaning AI needs.

Context Graph Defined

A context graph is a graph - nodes and typed relationships - but its organizing principle is business context for understanding, not merely an inventory. Its centre of gravity is the concept: a business idea like "customer," "revenue," or "churn," surrounded by everything that gives it meaning - its definition, the concepts it relates to, the data that implements it, the lineage behind that data, the policies that govern it, and the people accountable for it.

The goal is to capture, in machine-traversable form, the situational knowledge a domain expert applies automatically. When that knowledge is a graph, an AI can do what experts do: start from a concept and follow the relationships outward to assemble exactly the context a question requires.

Context Graph vs Knowledge Graph

The terms overlap heavily and are sometimes used interchangeably, but the emphasis differs in a useful way:

• A knowledge graph is the general structure - entities and relationships representing knowledge in any domain (it could model films, proteins, or people).
• A metadata knowledge graph applies that structure to the data estate - assets, columns, owners, policies - for governance and impact analysis.
• A context graph emphasises the business context for a consumer (usually an AI): the concepts, meanings, and relationships an agent must traverse to reason about a domain correctly. It often spans both business concepts and the data behind them.

In practice they are layers of the same idea. The context graph is best understood as the AI-facing view: the connected business meaning, grounded in the governed metadata graph beneath it.

What It Connects

A context graph weaves together the strands of context that, taken together, let something be understood:

• Concepts & definitions. The business ideas and their meanings - the glossary made relational.
• Data. The datasets, tables, and metrics that implement each concept.
• Relationships between concepts. How "churn" relates to "customer," "contract," and "revenue."
• Lineage. Where the data behind a concept comes from - its provenance and trustworthiness.
• Governance. The policies, classification, and ownership that say whether and how the data may be used.

Connected, these turn an isolated concept into a fully contextualised one - exactly what an agent needs before it answers.

Its Role in Grounding AI

The context graph is where grounded AI gets its grounding. Plain RAG retrieves isolated chunks of text by similarity, which can miss the relationships that make an answer correct. GraphRAG and agentic approaches instead traverse a context graph: starting from the concepts in a question and following relationships to gather connected, governed context - definitions, related concepts, source data, and trust signals. The result is an answer assembled from a coherent web of meaning rather than a bag of loosely-related snippets. This is why context graphs have become central to reliable enterprise AI: they give the model the same relational understanding a human expert brings, and they keep it inside governed boundaries.

How Dawiso Builds It

Dawiso builds the context graph from the governed knowledge it already manages, rather than asking you to model it from scratch. The business glossary supplies concepts and definitions; the catalog supplies the data that implements them; interactive lineage supplies provenance; classification and ownership supply governance - and AI-assisted enrichment proposes the relationships that connect them, so the graph grows without purely manual modelling. The Context Layer then serves this connected context to any AI agent through the Dawiso MCP Server, so an agent can traverse from a concept to all the governed context it needs in real time. The context graph is, in effect, what Dawiso turns your metadata into: not a list, but a web of meaning your AI can reason over.

Conclusion

A context graph is the connected model of business meaning - concepts, data, relationships, lineage, and governance - that lets an AI reason about a domain the way a human expert does: by starting from an idea and following the relationships that give it context. It shares its structure with knowledge and metadata graphs but is pointed at the question that matters most for AI: what context does the model need to answer correctly and safely? As enterprises ground their AI in their own data, the context graph is the substrate that makes those answers connected, trustworthy, and governed. Build it from your glossary, catalog, and lineage, serve it via MCP, and your agents stop guessing and start understanding.