Using context graphs: build a data moat like Google’s using your enterprise data

Enterprise software is on the cusp of leveraging a compounding data loop akin to those that have driven consumer tech giants like Google and Amazon, through the concept of context graphs built from decision traces. These traces serve as structured records of how decisions are made within organizations, capturing the reasoning behind choices rather than just the outcomes. Historically, enterprise reasoning has been ephemeral and not treated as data, but recent shifts—such as the digital transformation of work processes and advancements in AI—now allow for this reasoning to be observed and structured. By instrumenting agents to automatically record every decision and interaction, enterprises can create a living, queryable record of their decision-making processes. This offers the potential to mine these traces for patterns and insights, enhancing the decision-making capabilities of agents and allowing for continuous improvement. The article explores the implications of this development, emphasizing the strategic choices enterprises face between adopting proprietary platforms or maintaining open, portable decision history, with the opportunity to build durable business assets through the accumulation of decision traces.

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