Your semantic layer, automated: Meet Collibra's semantic agents

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According to Gartner, “Organizations that implement semantic modeling such as taxonomies and ontologies are up to 2.2 times more likely to achieve high effectiveness in data engineering practices supporting AI use cases compared to those that do not, yet only 40% have implemented these.” The reason is straightforward. Building a semantic layer has always been slow, manual, and difficult to scale, turning one of the most critical foundations of AI readiness into one of the hardest to build.

Introducing Collibra's semantic agents

Collibra is introducing two AI-powered agents purpose-built to automate the creation and connection of your semantic layer: the Semantic Model Generation Agent and the Semantic Mapping Agent.

Together, they eliminate the manual work that has long kept semantic layers incomplete, outdated and out of reach for teams without dedicated resources to maintain them.

How semantic agents help

The core challenge for any data-driven organization is connecting raw, technical data to its real-world business meaning. Without a reliable, efficient way to build that layer of context, the value of data stays locked away, inaccessible to the people and systems that need it most.

The Semantic Model Generation Agent tackles the traditionally tedious work of creating a semantic layer from scratch, while the Semantic Mapping Agent uses AI to deliver accurate, relevant suggestions for linking physical data assets to that model. The result is a faster, more accurate path from raw data to governed business context.

Problems it solves

For years, the task of linking technical column names to clear business definitions fell entirely on data stewards, a slow, manual and error-prone process that created significant bottlenecks across the organization. Incomplete data dictionaries hindered discovery. Outdated definitions weakened governance. And data teams found themselves unable to scale.

Collibra's semantic agents directly address three of the most persistent pain points:

Time-consuming manual mapping: The process of connecting physical data assets to their semantic representations has always been a significant drain on data steward time and organizational resources.
Error-prone and outdated data dictionaries: Manual data dictionaries are often outdated the moment they are finished. The agents provide active metadata enrichment, ensuring that the semantic layer remains a "living" foundation that instantly reacts to changing data and business circumstances.
Difficulty building a semantic layer from scratch: Establishing and maintaining a semantic layer has required a level of manual effort that most teams simply cannot sustain at scale.
Slow path to AI readiness: Without a governed semantic layer in place, AI agents lack the trusted, consistent context they need to operate reliably. Semantic agents accelerate the foundational work required to move from AI experimentation to AI at scale.

How semantic agents work

The Semantic Model Generation Agent and Semantic Mapping Agent operate through distinct but complementary AI-powered processes: one focused on creating the semantic layer and the other on connecting it to your physical data.

Semantic Model Generation Agent: Building the layer

The Semantic Model Generation Agent starts by analyzing what your organization already knows. It reads your existing business glossary, pulls calculation rules from your metric catalogs, and combines that business context with the technical metadata from your physical data assets: table names, column names, descriptions and classifications.

From these inputs, it automatically generates a business-friendly semantic layer of data entities and attributes, complete with descriptions and data classifications. It then instantly establishes the critical linkages, mapping new attributes back to physical columns and connecting them to the relevant business terms and KPIs.

What previously took weeks of manual effort now happens in a fraction of the time, with a mature, governed starting point from day one.

Map data attributes by selecting the specific data model and data entities to power AI-suggestions

Semantic Mapping Agent: Connecting the layer

Once your semantic model exists, the Semantic Mapping Agent takes on the work of connecting physical data assets to it.

A data steward identifies the target dataset. The agent then analyzes a rich combination of inputs: technical metadata, existing governance context like data classification labels and the target semantic layer, and uses advanced machine learning to generate accurate, relevant suggestions for mapping physical columns to their corresponding data attributes.

Creation of a Model and generation of Entities and Attributes

Stewards review and accept suggestions rather than building connections from scratch. Once approved, the agent automatically creates the linkages in the metadata graph, ensuring a complete and traceable connection from raw data all the way through to the business terms and KPIs it represents.

End result of semantic mapping process displaying physical columns mapped to data attributes

Who benefits

Data stewards get their time back. Automating the mapping process between physical and semantic assets dramatically reduces the effort required, allowing stewards to focus on higher-value, strategic governance work and enabling semantic mappings to be reused across the organization.
Analysts and business users work faster and with greater confidence, drawing on semantically enriched data they can trust rather than spending time validating definitions before every analysis.

And these AI-powered agents deliver significant value across key technical use cases:

AI agent enablement: Semantic agents ensure that every AI agent in the stack is working from a governed, consistent set of business definitions, eliminating hallucinations caused by ambiguous column names, conflicting metric definitions or missing business context.
Data product development: Semantic agents give data product teams the ability to rapidly attach governed business definitions, ownership, and classification labels to every data asset, transforming raw tables into trusted, self-describing data products.
Self-service analytics enablement: Semantic agents build the governed abstraction layer that allows analysts and business users to query data using familiar business terms, reducing dependence on data engineers, accelerating time to insight and improving the accuracy of self-service reporting.

Why this matters now

The pressure to build AI-ready data foundations is real and accelerating. But AI readiness is not just a data volume problem, it’s a data meaning problem. Without a governed semantic layer, AI models are left to interpret business context on their own. That is a risk no enterprise can afford.

Collibra's semantic agents represent a fundamental shift in how organizations can bridge the gap between technical data and business context, not by adding more manual work to already stretched data teams, but by embedding AI directly into the process of building and maintaining that foundation.

This is AI-powered governance in action: not as a promise, but as a practical capability available to your team today.

To learn more, read our documentation.

Meredith Bailey

Meredith Bailey

Senior Product Marketing Manager

Collibra
Krzysztof Nocuń

Krzysztof Nocuń

Staff Product Manager

Collibra
Monika Muskala

Monika Muskala

Senior Manager, Software Engineering

Collibra