Context Graphs: The Missing Piece from Pilot to Production?
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 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 It's outdated, contradictory across departments and missing the operational exceptions that actually matter.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 up to date. Status is always incomplete. Tacit knowledge can't be fully articulated. That's what makes it tacit. Healthcare runs on workarounds. Finance runs on judgment calls. That's not even the insight. Table stakes now.
The gap between "what's written" and "what's enforced" grows. Every working day.
The Documentation Gap
Fig 1"All prior authorization requests require 6 weeks of documented conservative treatment before imaging approval."
The policy is clear. The language is specific. The system ingests it verbatim.
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.
Related
For a deep dive on the garbage-in problem and why more documents make it worse, read: Garbage In, Garbage Out.
Garbage Has Reached the Foundation. What's the Fix?
A very reasonable response — the 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. The 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) captures 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.
""We followed the pattern" isn't a satisfying compliance answer. That's correlation dressed as explanation."
Trace ≠ Auditability
"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 Reconciliation Layer
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 regulations change — update the book.
Context graphs capture operational reality. SOPs capture organizational intent. Neither alone qualifies 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.
Context graphs capture operational reality. SOPs capture organizational intent. The foundation is the curated sync between them.
Continue reading: The Explainability Trap →
Vivek Khandelwal
2X founder who has built multiple companies in the last 15 years. He bootstrapped iZooto to multi-millons in revenue. He graduated from IIT Bombay and has deep experience across product marketing, and GTM strategy. Mentors early-stage startups at Upekkha, and SaaSBoomi's SGx program. At CogniSwitch, he leads all things Marketing, Business Development and partnerships.