Infrastructure became code.
Pipelines became code.
Policies became code.
Why is enterprise business meaning still scattered across catalogs, BI models, spreadsheets, wikis, and tribal knowledge?
I’ve been working on Semantics as Code, an open-source, vendor-neutral approach for defining enterprise business meaning as version-controlled, testable, and deployable artifacts.
The idea is to treat business semantics with the same engineering discipline we apply to infrastructure and data pipelines.
Business entities, metrics, relationships, glossary terms, ownership, governance metadata, quality expectations, and AI context can be defined as code, validated through CI/CD, reviewed through Git, and generated for downstream data and AI platforms.
For example, instead of allowing every dashboard, data product, or AI agent to independently interpret what Revenue, Customer, or Active Customer means, we can establish governed semantic definitions that are reusable across the enterprise.
Why this becomes especially important with AI agents
As enterprises adopt agentic AI, I believe we face a problem beyond traditional data governance.
Data governance can answer:
“Can this agent access this data?”
Policy governance can answer:
“Is this agent allowed to perform this action?”
But we also need to answer:
“What does this business concept mean, and which definition should this agent use in this context?”
I’m exploring this as Meaning Governance—using Semantics as Code as a foundation for providing authoritative, governed business context to AI agents.
The open project currently supports semantic definitions for entities, metrics, relationships, glossary terms, quality expectations, governance metadata, and AI context. The reference implementation also includes generation targets for platforms and technologies including Databricks Metric Views, dbt, OpenMetadata, knowledge graphs, and AI context artifacts.
Looking for collaborators
I’d love to collaborate with people in the Databricks community interested in:
Databricks Metric Views and semantic layers
Unity Catalog and data governance
Agentic AI / AI agents
Enterprise metadata and business glossaries
Semantic models and knowledge graphs
Semantic interoperability
Data contracts and data quality
Governance-as-code / policy-as-code
Building Databricks adapters and real-world examples
I’m particularly interested in exploring how Semantics as Code + Databricks + AI agents could work together to create a governed semantic foundation where business meaning is portable, testable, traceable, and consumable by both humans and autonomous agents.
The project is open source, and contributions, architectural feedback, use cases, criticism, and research collaboration are all welcome.
Project documentation:
https://vkondepati.github.io/semantics-as-code/
GitHub:
https://github.com/vkondepati/semantics-as-code
If this problem resonates with you, I’d love to connect and collaborate.
Define once. Govern everywhere.
Venkat
