Context Graphs and Process Knowledge

It's The Same Thing

The Decision Trace Problem

Foundation Capital’s recent opinion article about context graphs identifies a critical gap in enterprise systems, and I couldn’t agree more with the problems identified. Organizations have mastered recording what happens, but they remain unable to capture why decisions are made. When a VP approves a discount that exceeds policy limits, the reasoning is stuck in Slack threads, hallway conversations, and institutional memory. When a maintenance technician changes a procedure step, the justification exists in their head, not within online systems. These decision traces—the contextual logic, precedents, and judgments that animate organizational workflows—disappear the moment they’re executed.

A context graph, in theory, is positioned as a solution to this tacit and implicit knowledge problem. The proposed solution introduces the concept of a living record of decision traces, stitched across entities and time, where precedent becomes searchable. Stitching the data together, syntactically. Unlike traditional systems of record that store objects and outcomes, context graphs capture the reasoning that connects inputs to outputs. This creates what Foundation Capital calls the “single most valuable asset for companies in the era of AI” because AI agents need access to decision traces to handle the ambiguous, judgment-laden situations that humans navigate through organizational memory.

Building such a system requires more than database architecture. It demands a formal methodology for representing how work happens, tracking how procedures are executed, in practice, and linking decisions to their justifying context. Which is what the Procedural Knowledge Ontology (PKO) and process knowledge management frameworks provide.