From RAG to ontology: Databricks bets on context as the key to trusted AI agents

First came vector databases, then RAG. Now, the next frontier in enterprise AI is taking shape: context layers that give autonomous agents a shared understanding of the business, a vision Databricks is advancing with Genie Ontology.

Currently in preview, Genie Ontology automatically extracts business context from enterprise data, dashboards, queries, pipelines, documents, and applications and organizes it into a living graph that AI agents can use to understand how an organization operates.

Showcased at the company’s Data + AI Summit, Genie Ontology uses a ranking system inspired by Google’s PageRank to identify the most authoritative business definitions within an organization.

Rather than treating all sources equally, it weighs factors including who created the information, how widely it is used, its links to certified datasets and assets, and how recently it was updated before determining which answer an AI agent should rely on, Databricks CEO Ali Ghodsi said during his keynote late on Tuesday while explaining the new offering.

Organizations can also upload their own business definitions or ontologies to Genie Ontology via Databricks’ existing Unity Catalog Semantics platform, Ghodsi added.

Ontology promises consistency, but readiness remains a hurdle

For CIOs, a unified context layer, such as Genie Ontology, will materially improve consistency, trust, and governance for enterprise AI deployments, according to analysts.

“One definition feeding every agent means you stop getting three different answers to the same question,” said Michael Leone, principal analyst at Moor Insights and Strategy.

“Older approaches, such as RAG and vector search, just pull back whatever looks similar to your question, and they don’t actually understand your business. An ontology gives the agent the meaning a catalog can’t, what your terms mean, and which source to trust,” Leone added.

That improvement in consistency, according to Ashish Chaturvedi, leader of executive research at HFS Research, could also improve trust, which remains one of the most critical barriers to AI adoption.

“The single biggest barrier to enterprise AI adoption is that decision-makers don’t trust AI outputs enough to act on them without checking. An ontology that grounds answers in governed business definitions, with lineage back to source, directly attacks that trust deficit,” Chaturvedi said.

Alternatively, Leone was more cautious about the trust argument: “It’s a promising idea, but it still has to prove itself before I’d lean on it for anything that matters.”

Echoing Leone, HyperFRAME Research’s practice leader of AI stack Stephanie Walter pointed out that ontologies have a missing link, and that is verification: “Ontologies can improve context, but they do not guarantee the answer is correct. An agent can still pull incomplete data, apply the wrong logic, skip rows, misunderstand a workflow, or take the wrong action.”

That verification gap becomes even more critical, according to Leone, because most enterprises don’t have the data and governance readiness required to implement an ontology layer for AI deployments: “If your data and governance aren’t already in order, this just speeds up your existing mess.”

Seconding Leone, Walter pointed out that an ontology cannot fix messy definitions, poor lineage, weak ownership, or fragmented permissions on its own.

Additionally, the analyst pointed out that the hard part for CIOs is not creating an ontology once but keeping it accurate as the business changes: “Enterprises will need clear data ownership, metric ownership, domain expertise, governance processes, and a way to resolve conflicting definitions.”

“Otherwise, the ontology becomes another stale metadata project with a more sophisticated name,” Walter added.

A growing risk of CIO confusion

Beyond data and governance readiness, CIOs also face a growing risk of confusion in the wake of several technology vendors pursuing approaches, similar to Genie Ontology, to ground enterprise AI in a business context, according to analysts.

Over the past year, Snowflake, Microsoft, and others have introduced some form of ontology, semantic, and context-layer offerings, but the problem is in how these offerings are named, Leone said.

“Everyone slapped a different name on basically the same idea. It slows people down as it creates confusion,” Leone noted.

That confusion could also backfire on Databricks and other vendors, according to Bhupendra Chopra, cofounder and CRO of IT consulting firm Kanerika: “While the marketing has converged around context-building offerings, most enterprises will choose the platform where their data already resides.”

HFS Research’s Chaturvedi doubled down on that view, saying CIOs should resist evaluating ontology offerings in isolation and asked them to stick to the mantra of context layer follows data gravity: “If your data lives in Databricks, Genie Ontology is your path. If it’s in Snowflake, Horizon Context is. If you’re a Microsoft shop, the IQ family is.”

Additionally, Chaturvedi urged CIOs to look beyond functionality and assess how open and portable these offerings are, particularly in multi-platform environments where business definitions may need to move across data lakehouses, analytics tools, and AI platforms.

This is where Chaturvedi sees Snowflake differentiating itself from rivals, with its focus on open semantic interoperability aimed at reducing the risk of semantic lock-in as enterprises evolve their data and analytics stacks.

The battle for the AI control plane

Snowflake’s efforts to differentiate itself, though, analysts pointed out, at least for CIOs, draw attention to a larger race among vendors, including Databricks, to become the control plane for enterprise AI.

While Snowflake is attempting to position itself as an AI control layer through a combination of Snowflake Intelligence, Horizon Catalog, and its push for open semantic interoperability, Microsoft is embedding business context and governance across its Copilot, Fabric, and broader AI stack through offerings such as Work IQ, Fabric IQ, and Foundry IQ, Chaturvedi said.

Databricks’ Genie Ontology, too, is part of a similar strategy, Chaturvedi pointed out, urging CIOs to view the offering in the context of the company’s wider effort to position its lakehouse platform as the foundation on which enterprise AI agents are built, governed, and eventually deployed.

“It’s absolutely a control-plane play. When you connect the dots across everything Databricks has announced at this summit, including LTAP, OpenSharing, and Genie Ontology, you see a single place where enterprise data, governance, business semantics, and agent execution all converge,” Chaturvedi added.

Further, the analyst noted that the control-plane strategy reflects Ghodsi’s broader vision that data platforms could evolve into what the CEO describes as an “agentic system of record” — an authoritative source that AI agents read from, reason over, and act through.

The concept mirrors earlier platform shifts, Chaturvedi said, where ERP systems became the system of record for business transactions and data warehouses became the system of record for analytics.

The next battle, the analyst said, is over which platform becomes the system of record for enterprise AI agents.

Moor Insights and Strategy’s Leone agreed that data platforms are well-positioned to compete for that role because they already own the data, governance controls, lineage, and permissions that agents require to operate safely at scale.

Still, analysts cautioned that context alone will not determine which vendor comes out on top.

“The next enterprise AI battleground is not just context. It is verifiable execution,” Walter said.

The article originally appeared on InfoWorld.