Are We Entering the Context Engineering Era?

[Brahmareddy]

I’ve been thinking about how enterprise AI is changing, especially after attending the Data + AI Summit 2026 and hearing Ali Ghodsi emphasize the importance of enterprise context. It made the direction much clearer to me. We already have very capable AI models. The bigger challenge now is giving those models the right understanding of the business.

What does this data mean?

Which metric should the AI trust?

Who can access it?

What business rules should it follow?

And what action should it take next?

That also made me think more about the 4Cs: Context, Control, Choice, and Cost. Context helps AI understand the business. Control helps enterprises govern what AI can access and do. Choice gives organizations flexibility across models and tools instead of locking everything into one path. Cost becomes critical when AI moves from a few experiments to thousands or even millions of agent actions.

This is why I find the direction Databricks is taking with Genie, Genie Ontology, Unity Catalog, and agents very interesting. We spent years building platforms that make enterprise data usable for people. Now we are entering a phase where that same data, meaning, governance, and business knowledge need to become usable by AI agents.

For me, the 4Cs are becoming a simple way to think about enterprise AI: give AI the right Context, keep the right Control, preserve Choice, and understand the Cost.

Would love to hear how others in the Databricks Community are thinking about this.

[kartikchoudhary]

This is an interesting way to look at it. I think we're moving from “can the model answer the question?” to “does the model actually understand the business context behind the question?”

You can have a very capable model, but if the underlying definitions, permissions and business rules aren't clear, the answer can still be wrong. That's where I think the data and governance side becomes really important.

[Brahmareddy]

Exactly, Kartik. I think this is where the shift becomes important. A powerful model is only one part of the equation. If definitions, permissions, lineage and business rules are unclear, even the smartest model can confidently give the wrong answer. Context and governance increasingly have to move together.

[Niyojit]

Completely agree. AI agents are only as effective as the context they're given. As models become commoditized, context engineering becomes the real differentiator. Tools like Genie, Genie Ontology, OntoBricks, and Unity Catalog show how enterprise knowledge can be transformed into something AI agents can truly understand and act on. That's what will drive the next phase of Agentic AI.

[Brahmareddy]

Well said, Niyojit. As models become more capable and interchangeable, the enterprise context around them becomes much more valuable. The real advantage may not be having the smartest model, but having AI that truly understands your data, business language, rules and decisions.

[nick_martinek]

There is one thing I'd add which is that many large enterprises still struggle with governance, ownership and sometimes even with common definitions of what their data means. The interesting question is not only how much context we can give to tools like Genie, but if that information is reliable enough in the first place. Maybe the real test is whether AI can operate safely even when company governance is incomplete and constantly changing.

[Brahmareddy]

Great point, Nick. I think this may actually be one of the hardest parts of context engineering. AI cannot magically fix unclear ownership, conflicting definitions or outdated business rules. In some ways, AI will expose these gaps faster.

The next challenge may be building context that is not just available, but also governed, traceable and continuously updated as the business changes.