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Context Graphs: Hype Meets Reality Check

A concept is making the rounds in AI circles right now that has many people very excited: context graphs . Foundation Capital published a piece calling it "AI's trillion-dollar opportunity." Engineers and founders are writing technical breakdowns of how it would work, and VCs are looking for startups building in this space. The basic premise is simple but powerful: our systems capture what happened, but not the why . And in an agentic world, that "why" becomes critical. The idea is elegant, intellectually compelling, and really appeals to the systems thinker in me. Plus, it has the word "graph" in it, and I LOVE graphs. Have loved them for decades. Fun fact, the HubSpot logo is a zoomed in look at a graph (with a node in the middle). The idea underlying context graphs is very powerful, but I think we need a reality check about where companies actually are versus where this conversation assumes they are. So let's break down: What context graphs actually are Why smart people think they're important Where I think the hype meets reality What Is a Context Graph? Here's the core idea: most of our current systems capture what happened, but not why it happened. Why did this deal need to be escalated to legal review? Why did we pick Providence, RI for our next retail store? Why did we decide to discontinue product [X]? That reasoning -- the decision traces, the exceptions, the precedents -- lives scattered across Slack, work calls, and inside people's heads. It's insider knowledge that builds up as employees gain experience and resets every time someone leaves. A context graph is meant to capture all of that systematically. Not just the final state, but the full sequence of decisions: what inputs were considered, what policies were evaluated, what exceptions were granted, who approved what, and why. It's a system of record for decisions , not just data. I think of it as a system of reasoning . (But I’m not promoting that as a phrase, because it’s easily confused with the reasoning that an LLM does). Why Smart People Are Bullish The argument for why context graphs are important comes down to agents. As AI agents begin handling real workflows -- reviewing deals, resolving tickets, and more -- they run into the same gray areas humans face in everyday work. Humans handle those situations using judgment and insider context built through experience, but agents don't have access to that layer. They see the final state in the CRM, not the reasoning that led there. Context graphs are supposed to solve this. By capturing decision traces as agents work, you build a queryable history of real-world precedents. Over time, exceptions become encoded knowledge. The organization stops relying on oral tradition and starts learning from its accumulated actions. Smart folks like Jaya Gupta at Foundation Capital are making compelling cases . Startups building "systems of agents" could have a structural advantage because they sit in the execution path -- they see the full context at decision time. The theory is elegant. Why I’m A Wee Bit Skeptical But here's the thing about elegant ideas: history is full of concepts that were intellectually compelling but didn't take off in practice. The reason is usually the same. They were just a tad too abstract. To get from "here" to "there," you need infrastructure, cooperation, and adoption that doesn't exist yet. You a path from here to there and need to build bridges and tunnels to get around the obstacles you will invariably run into. And right now, my take is that most companies are nowhere near ready for context graphs. We’re barely at the point where semi-autonomous agents are getting deployed for some key use cases (like customer service). Companies are still struggling with basic data unification. They're still trying to get their CRM, support system, and product data to talk to each other. They're early in their adoption cycle of AI -- figuring out if an AI assistant can handle tier-1 support. Agents -- whose activity is supposed to generate the decision traces that populate the context graph -- are themselves very early and not widely adopted. Asking companies to capture decision traces when they are still bringing their data efforts in order and haven't even deployed agents at scale yet is sort of like asking someone to install a three-car garage when they don't own a single car. I'd love to live in the world where context graphs exist. That's why HubSpot is building toward that kind of future as part of our agentic customer platform. It's an important piece of the puzzle. But I think we need to be more pragmatic about the timeline and our expectations. Most businesses are still figuring out how to use AI to drive real, tangible value. They're not ready to instrument their agent orchestration layer with decision traces. And that's okay. That's the reality of adoption curves. Context graphs (or something like it) are a beautiful idea that will matter eventually. It feels inevitable. The question is when that "eventually" arrives, and what has to happen between now and then to make it real. If you're building in this space, I'd love to hear what you're working on (just let me know by leaving a comment -- I read all of them). Thanks. - @dharmesh