Context Graphs: Transformational Architecture Or Familiar AI Hype?

Verdantix research shows growing adoption of knowledge graphs, which form a critical part of the enterprise data layer servicing AI agents. Enter 2026, and the concept of context graphs has hit the forefront of knowledge graph and AI data layer discourse. Foundation Capital’s blog, in particular, has made a splash – alongside further conversation from Kirk Marple, Venkat Venkatraman and enterprise AI platform firm Glean.

So, what is a context graph? Context graphs aim to build on knowledge graphs that encode information as subject–predicate–object triples or RDF statements, by adding a contextual dimension, often implemented through quadruples or reified relationships, to capture provenance, temporal validity, authorizations and exceptions alongside factual relationships. Because of this, context graphs are valuable in enterprise environments where governance, compliance and semantic alignment across teams are vital. Currently, a small set of vendors – including Neo4j, Precisely and TigerGraph – as well as open-source projects such as TrustGraph, are exploring context-rich graph approaches. However, true context graph implementations remain immature in enterprise, with most usage confined to pilots and research projects. Furthermore, the distinction between a knowledge graph and a context graph is incremental rather than transformational. The core challenges of data integration, ontology alignment and governance remain, while the cost and complexity of maintaining the graph increase.

Information on decision context often sits siloed across enterprise systems, buried in emails, Slack or Teams conversations, or informal discussions. By embedding it within a structured data store (through context graphs), enterprises hope to make AI agents more capable in exception-heavy workflows that require traceability and auditability. However, a lot of valuable context is often unrecorded or too ambiguous to translate cleanly into structured representations. The rise of return-to-office policies, from firms including Paramount and Novo Nordisk, has further reduced the amount of decision-making captured in digital systems, creating blind spots that no context graph can reliably fill.

In practical deployments, context graphs could be used to capture approvals, exceptions and semantic differences in agent-driven workflows, leveraging information such as when a sales manager authorizes a discount exception in a Slack message. While this appears attractive in theory, the nuances of enterprise language, including hedging, authority gradients and social cues, are extremely difficult for AI systems to interpret consistently. Inferring intent from ambiguous statements risks encoding one-off exceptions or decisions as repeatable workflow pathways, potentially propagating incorrect precedents or introducing non-compliance risk. As a result, initial context graphs will be best suited to environments where context is explicit and standardized, conditions that few enterprises sustain over time.

There is also a structural constraint on the type of context that can be captured. Low-stakes, high-frequency procedural decisions are far more likely to be documented and formalized than complex, rare or sensitive ones. Over time, this leads to context graphs that are dense with bureaucratic detail but sparse in substantive judgment. The first iteration of context graphs will lead to more effective automation of existing workflows rather than a meaningful shift in what agents can do.

Given these constraints, context graphs should be seen as a transitional technology that helps AI agents better navigate and automate existing human workflows, especially in regulated or exception-heavy domains. They offer a pragmatic response to the messiness of human workflows, improving efficiency, consistency and traceability where conditions allow. To learn more about advanced data ontologies and emerging enterprise AI trends, visit the Verdantix AI Applied insights page and tune in to our webinar on predictions for 2026.