Agent Context Graphs: Color Me Skeptical
· #ai
Over the past few weeks, I’ve heard repeated mentions of this idea of “enterprise context graphs” paired with AI agents.
One representative description goes like this:
”.. the missing layer that actually runs enterprises: the decision traces – the exceptions, overrides, precedents, and cross-system context that currently live in Slack threads, deal desk conversations, escalation calls, and people’s heads. […] Once you have decision records, the ‘why’ becomes first-class data. Over time, these records naturally form a context graph: the entities the business already cares about (accounts, renewals, tickets, incidents, policies, approvers, agent runs) connected by decision events (the moments that matter) and ‘why’ links.”
What’s interesting, and perhaps somewhat telling, is that the only people I’ve seen talk about these “context graphs” are VCs, not engineers.
I’ve seen firsthand three attempts to create a unified data & domain model across just one slice of an enterprise fail after years of effort. Many CTOs I know have similar stories. We all have scars from this.
The reason is simple. It is genuinely hard to unify the complexity of a real company into a clean data model. Hard enough to capture end-to-end what a large company does, let alone why!
So I remain skeptical that such a “context graph” can be built in any clean or durable way.
But wait. The argument is that AI agents change the equation.
Because agents sit directly within business workflows, the claim is that they are naturally exposed to the “why,” and can capture decision traces as decisions are made, allowing a context graph to emerge organically over time.
To quote again:
“When an agent triages an escalation, responds to an incident, or decides on a discount, it pulls context from multiple systems, evaluates rules, resolves conflicts, and acts. The orchestration layer sees the full picture: what inputs were gathered, what policies applied, what exceptions were granted, and why. Because it’s executing the workflow, it can capture that context at decision time – not after the fact via ETL, but in the moment, as a first-class record.”
I find this logic suspect, because the hard problem here is not data capture. It is representation.
Capturing traces and rationales is one thing. Representing them in a structured graph of entities and relationships is where things repeatedly break down. That modeling problem doesn’t disappear just because an agent is in the loop.
And if the claim is that structure doesn’t really matter because AI can figure it out anyway, then it’s not clear why we need to talk about a “context graph” at all! AI should already be able to infer what it needs from Slack threads, deal desk conversations, escalation calls, and people’s notes.
I can’t help but think that this is less about a real, unmet need, and more about naming and inflating a new startup category that glosses over some very old, very hard problems.