We asked an AI for quarterly revenue. It said $12M.
The actual answer was $8.4M.
Not because the model was bad. The data was all connected. The AI just had no way of knowing whether "revenue" meant revenue_recognized or revenue_net_of_returns in this company.
Why does an AI get it wrong when all the data is there?
Most people blame the model when enterprise AI deployments fail. But that's usually not the issue. According to a March 2026 survey by Cloudera and Harvard Business Review, only 7% of companies say they're truly ready for AI adoption. 88% say the context exists — yet 61% are still delaying AI adoption because that context isn't actually accessible.
The reason? There's too much in every company that AI simply doesn't know. What "revenue" means. What "customer" refers to in the CRM versus the billing system. Who has access to which files. These things are buried in Slack threads, in someone's head, or defined 27 different ways across the organization.
Jedify co-founder Assaf Henkin put it plainly: "Without business context, agents either hallucinate or burn tokens processing irrelevant information."
The core insight
AI agent failures aren't a model performance problem. The data exists, but the meaning doesn't — the AI doesn't know how the business actually works.
Why RAG and knowledge graphs don't solve this
The common fix is RAG. But RAG retrieves text documents — it can't deliver "exactly what does 'revenue' mean in this context right now" to an agent in a structured, reliable way.
Knowledge graphs are more structured, but they lock in relationships at design time. Deciding at runtime who can access a specific file, or whether there's a precedent for an exception — that's hard to do dynamically.
| RAG | Knowledge Graph | Context Graph | |
|---|---|---|---|
| Role | Document retrieval & injection | Entity relationship structuring | Business meaning + runtime judgment |
| Updates | Requires re-embedding | Fixed at design time | Continuously auto-updated |
| Permission awareness | None | Limited | Permission nodes built-in |
| Decision tracking | None | None | Decision history preserved |
| Agent autonomy | Supplementary | Partial | Full autonomy possible |
An Atlan AI Labs benchmark found that adding semantic metadata to context improved AI-generated SQL query accuracy by 38%. Gartner predicts that by 2028, 50% of enterprise AI agent systems will depend on context graphs.
Context graphs: the "company brain" for your agents
Context graphs emerged to fill this gap. In June 2026, New York startup Jedify raised a $24M Series A, putting this category squarely in the spotlight. Norwest led the round, with Snowflake Ventures as a strategic investor. Total funding now exceeds $33M.
Here's what a context graph actually does. It connects structured data (data warehouses, CRMs, financial systems) with unstructured knowledge (documents, Slack threads, meeting transcripts). And it goes beyond simple connections. What "revenue" means precisely in this company. Which employee can access which data. How a past exception was handled and what precedent it set — all stored in a form agents can query at runtime.
Jedify uses Semantic Fusion™ technology — automatically fusing structured data with unstructured knowledge to create a semantic model agents can actually use. Kore.ai's analysis noted a key advantage: every time an agent makes a decision, the graph gets more accurate. A system that improves with use.
Foundation Capital frames it similarly. Context graphs capture not "what the docs say things should be" but "how things actually work." They extract the knowledge buried in Slack threads, hallway conversations, and people's heads — and structure it into something AI can act on.
How to apply a context graph in your team
Successful companies don't start company-wide. They start with one workflow, one sharp pain point.
- Find your context gaps
List the cases where your agent repeatedly fails or escalates to a human. These are the spots a context graph needs to fill first. - Start with one metric
Pick one — like "revenue" — and converge on a single authoritative definition. That's your context graph seed. - Connect your data sources
Use a platform like Jedify or build your own ontology. Connect both structured data (DW, CRM) and unstructured knowledge (Slack, docs). - Add the permissions layer
Encode who can access what in the graph. Agents need to respect these permissions autonomously to be safe in production. - Validate at small scale, then expand
Measure whether agent error rates drop on one workflow. When they do, expand. Jedify is model-agnostic — switch AI providers later and your context graph comes with you.
Watch out
When evaluating context graph platforms, check for vendor lock-in. Being tied to a specific AI model creates expensive switching costs later. Model-agnostic platforms like Jedify are the safer long-term bet.
Go deeper
Jedify: Context is King in the Era of AI Agents Why context is the decisive variable in the agent era — written by the Jedify team jedify.com
Atlan: Context Layer for AI Agents Enterprise guide covering 5 key components and the difference between RAG and context layers atlan.com
Kore.ai: What Are Context Graphs and How Do They Make AI Agents Smarter How decision history and precedent patterns make AI agents smarter kore.ai
Atlan: Context Graph vs Knowledge Graph 2026 Five key differences and a guide for when to use which atlan.com
Diginomica: Context graphs unlock a new seam of enterprise knowledge How context graphs surface decision knowledge buried in Slack and email diginomica.com
Jedify $24M Series A announcement Official press release with Semantic Fusion™ details and investor info globenewswire.com
