Context graphs are suddenly becoming a hot topic in AI/knowledge graphs

Ever since Jaya Gupta from Foundation Capital described context graphs as a potential trillion-dollar opportunity, the term has started popping up more and more in conversations around AI infrastructure.

Interestingly, the idea itself isn’t completely new.

At Knowledge Graph Conference 2025, Henry Su presented a session explaining how AbbVie built a context graph using metadata from their ARCH knowledge graph.

The core idea is pretty straightforward:

A context graph is essentially a knowledge graph that captures the decision process —
all the steps, approvals, and signals that went into making a real decision.

Instead of just storing data, it stores the decision trace.

That trace can then be used to inform AI systems — especially agentic AI, where systems need to make decisions rather than just retrieve information.

One interesting point:
For many organizations, documenting decision traces is actually new.

But in highly regulated industries, it’s not.

Systems like GRC (governance, risk, compliance) already record decisions in detail.
Traceability is critical in industries like pharmaceuticals, agriculture, and food supply chains so that issues like recalls can be handled precisely.

There are also adjacent concepts like journey analytics — mapping customer journeys, patient journeys, or student journeys.

What does feel new is combining this kind of structured decision trace with AI agents.

You could train agents on SOP documents, but SOPs often miss:

• real-world variability
• edge cases
• exceptions that happen in practice

Detailed decision traces might actually be a better training signal.

Whether we call them decision graphs or context graphs, the idea of capturing how decisions are actually made could become an important layer for agentic systems.

Curious if others here are seeing the same trend —
Are context graphs actually a new category, or just a rebranding of existing knowledge graph patterns?