Note: Publishing this on a Monday morning because Context graphs are more causing more confusion than expected. Moving forward, newsletter will hit your inbox on every Thursday 10 AM.
Context graphs are having a moment. I am already hearing versions of interpretations. Let’s break down what exactly is the concept of context graphs, why it’s a great idea but in isolation - it's moves nothing.
Here’s what you already know?
Documentation is a mess. Not the "data-in-silos-mess". It's outdated, contradictory across departments and missing the operational exceptions that actually matter. It’s universal. Small businesses and Enterprises. All categories - Healthcare, Consumer, Retail and more. Yet somehow enterprises manage to run on this state of the documentation.
Truth be told - Documentation is, and will never be upto date. Status is always incomplete. In some sense. Tacit knowledge can't be fully articulated. That's what makes it tacit. Healthcare runs on workarounds. Clinical research. Finance runs on judgment calls.That's not even the insight. Tablestakes now.
The gap between "what's written" and "what's enforced" grows. Every working day.
So What’s the Hoopla All About?
Human teams magically route around bad documentation. They know which SOPs to ignore, which policy is actually enforced, which version is current even when the doc says otherwise.
But.
AI agents don't have that “tribal knowhow”. They ingest the book as is ( SOPs, Policies, everything that's written). The not-so-updated book. Agents, now suddenly have a "garbage-in" problem.
So Garbage has reached the foundation. What’s the Fix?
A very reasonable response (Context Graph proposition) - If the book is incomplete, let's go around it? Capture what people actually do. The tribal knowledge. The patterns that work regardless of what's documented.Context graphs say: stop trying to fix the book. Route around it. Capture the traces of actual decisions. Build the intelligence layer from reality.
This feels like the right answer. The documentation update process is broken or too slow and feels unfixable. Context graphs approach offers an end-run. Capture what people do. Formalize what works. Skip the mess. Problem solved?
Just one problem - You can't route around something you also depend on. Tribal knowledge (Context graphs) capture correlations, not causation. They are joint-at-the-hip with the very SOPs they're trying to route around. In regulated industries, this dependency can't be ignored.
Tribal Knowledge (Context graphs) capture patterns: When condition X occurs, expert Y does Z.
These inputs correlate with successful outcomes. This sequence tends to get approved.
What's needed (and they should capture)(also requires curation):
Why it works.- Is action Z correct because of policy Q? Or does it work despite violating policy Q?
Would it work next quarter if guidelines change?
The expert might know. Or they might not. Remember - many experts can't articulate why their approach works.
"We followed the pattern" isn't a satisfying compliance answer. When the auditor asks why the agent did X, "because experts usually do X" doesn't close the loop. That's correlation dressed as explanation.
This is the explainability trap - Agents work but can't prove why.
The opportunity isn't unlocked with just tribal knowledge OR documentation. It comes together with the reconciliation layer.
When the workaround aligns with policy - formalize it. Update the book.
When the workaround violates policy - surface it. Decide intentionally.
When the regulations change, update the book.
Context graphs capture operational reality. SOPs capture organizational intent. Neither alone qualify to be the foundation. The foundation is the domain expert curated sync between them. Get that right, and you would have tackled the first problem - garbage-in. Head on. At the source - not routed around it.
That's also where explainability actually lives.
Continue reading here > The Explainability Trap →
Best
Vivek K