There's a concept getting a lot of attention in AI infrastructure circles right now: the context graph. Foundation Capital recently called it a "trillion-dollar opportunity", which is the kind of VC-speak that usually makes me tune out. But they're onto something real here.
Let me explain what context graphs actually are and why they matter for anyone building AI-powered tools.
The problem with how AI remembers things
Most databases store what happened. Customer X bought Product Y on Date Z. Simple.
But here's what they don't store: why it happened.
Say your VP approves a 40% discount for a big customer. A normal system logs the discount. A context graph captures that Sarah approved that specific discount because the customer committed to a three-year contract.
That "because" matters. Next time an AI agent needs to decide whether to approve a similar discount, it has precedent. It knows what reasoning worked before.
This is the core idea: context graphs don't just store data, they store decision traces—the reasoning behind actions.
From "systems of record" to "systems of agents"
Here's the interesting shift: for decades, the most valuable enterprise data lived in systems like Salesforce, Workday, and SAP. These are "systems of record"—they store what happened.
But as AI agents start doing real work (not just answering questions), they need something different. They need to understand how to make decisions, not just have access to historical data.
That's where context graphs come in. They become the source of truth for AI agents—telling them not just what the company did, but why, and under what circumstances.
One way to think about it: context graphs are like institutional memory that AI can actually use.
What makes a context graph work
A few things distinguish context graphs from regular databases:
Time matters: It's not enough to know that something happened. You need to know when it was true. Policies change. The discount that made sense in Q1 might not apply in Q4. Good context graphs track this.
Decisions, not just data: The core value is capturing the "why." As one researcher put it, it's "just enough structure to explain why an action occurred, not everything that exists."
Rich metadata: Beyond the basic facts, you want to track:
- When something happened
- Who made the decision
- How confident we are in the data
- Where it applies (one team? The whole company?)
Who's actually building this stuff
A few companies are putting context graphs into production:
Regie built their sales platform around AI agents that can prospect, write outreach, and follow up—all while knowing when to escalate to a human. The context graph tells them what worked before.
Maximor does accounting automation. Their AI knows why certain transactions were categorized certain ways, so it can apply the same logic to new entries.
Graphlit is building infrastructure specifically for this—ingesting content and turning it into time-aware, identity-resolved context that AI agents can actually use.
Why we care about this at Trilo
For teams using collaborative AI (like Trilo), context graphs solve a real problem: keeping AI coherent across multiple people and conversations.
Andrej Karpathy said something recently that stuck with me: "Context Engineering is the new Prompt Engineering." He's right. The quality of AI output increasingly depends not on how cleverly you phrase a single prompt, but on what context the AI has access to.
Context graphs let you:
Share knowledge automatically: When one person's AI learns something useful, that understanding can propagate to the whole team.
Let AI know when to ask for help: With enough decision precedent, AI agents can figure out which situations they can handle and which ones need a human.
Build real institutional knowledge: As Arize AI points out, the decision traces you accumulate become genuinely valuable. They're how your AI gets smarter over time.
If you want to start thinking about this
Here's where I'd begin if I were building context-aware AI systems today:
Figure out where decisions happen: Map out the workflows where people make judgment calls. Those are the places where decision traces are most valuable.
Start logging the "why": Even if you don't have fancy infrastructure yet, just start capturing reasoning. Why did we approve this? Why did we reject that? It compounds.
Track when things change: Decisions that made sense in one context might not apply in another. Make sure you're capturing the timing.
Design for multiple users: If you're building for teams, think about how context gets shared. What does one person's AI learn that could help everyone else?
This is still early. Most companies haven't figured this out yet. But the ones that start building context infrastructure now will have a serious advantage when AI agents become more capable and more central to how work gets done.
We're building Trilo to be context-aware from the ground up. If you're interested in AI that actually remembers what your team has done and why, check us out.
Further reading:
