Can Apache Ossie Become the Unified Business Language for AI Agents?

1. Business semantics remain siloed despite cross‑platform data access

Enterprises can read data across different engines using open file formats, tables and catalogs, but metrics, dimensions, entity relationships and business rules stay locked inside separate data warehouses, BI platforms and semantic‑layer products. The same concept such as "revenue" or "monthly active users" can produce different results across systems, causing metric drift for traditional BI and potentially incorrect queries for AI agents.

2. Apache Ossie enters the Apache Incubator

On July 8 2026 Snowflake announced that the former Open Semantic Interchange project has been renamed Apache Ossie (Incubating) and entered the Apache Incubator. The project name and governance changed, but the original specification, community goals and the YAML‑based format for describing metrics, dimensions and relationships remain unchanged.

3. Ossie is an open model spec, not a replacement BI product

Ossie is not positioned as a replacement for Looker, dbt, Snowflake or other semantic‑layer execution engines. Instead, it provides an open model specification that first unifies how semantic objects are described and exchanged, and later plans to address more complex metric semantics, ontology mapping and query execution.

4. Data can be read cross‑platform, but semantics are still tool‑bound

Physical table structures tell a system which fields exist and their types, but they cannot answer how those fields should be used business‑wise. For example, an order table may contain amount, tax, discount and status, leading to multiple valid SQL expressions for revenue. Semantic layers translate such physical structures into business objects (metrics, dimensions, entities, relationships, calculation rules) so users can query "revenue" without rebuilding field logic each time. Currently many metrics are stored in proprietary formats such as LookML, YAML, SQL or internal metadata, forcing organizations to recreate identical logic when adding new BI tools or AI applications.

5. Ossie as a neutral intermediate format

The Apache Ossie website describes the project as an open specification for analysis, AI and BI platforms, emphasizing vendor neutrality, YAML configuration, model interoperability and AI‑focused semantic context. Platforms can keep their own modeling language and execution engine, but import, export or share models through Ossie, reducing the need for point‑to‑point adapters. The project adopts a hub‑and‑spoke architecture: a central Ossie core spec with converters for each platform (dbt, GoodData, Honeydew, OrionBelt, Apache Polaris, Salesforce, Snowflake, etc.). Reference implementations exist, but complex metrics and vendor‑specific capabilities may still require additional mapping.

6. Core specification objects

The current core spec defines a top‑level semantic_model containing datasets, relationships, metrics and custom_extensions. Datasets describe logical data collections and point to physical sources; fields include primary/unique keys and context; relationships describe joins via from, to, from_columns and to_columns. Metrics store aggregation expressions, and fields can hold multiple dialect expressions (ANSI SQL, Snowflake SQL, BigQuery, MDX, Tableau, etc.). This design enables expression migration across platforms but does not guarantee identical execution semantics.

7. ai_context for AI‑oriented metadata

The spec introduces an ai_context field that can appear on models, datasets, relationships, fields and metrics. It may contain instructions, synonyms and examples to help AI agents map natural‑language queries (e.g., "sales" or "total revenue") to a unified metric. While this reduces hallucination risk, it does not replace data quality, model selection, query planning, access control or result verification.

8. Governance through Apache

Moving to Apache provides a neutral governance model: the project uses public mailing lists, GitHub collaboration, formal discussions and voting, with committer status based on contributions rather than corporate affiliation. The incubator started on 19 June 2026 and entered incubation on 22 June 2026. Contributions have come from Snowflake, Salesforce, Databricks, dbt Labs, RelationalAI, GoodData, Honeydew and others, but the incubator status does not imply maturity; the current version is 0.2.0.dev0 and is not recommended for production.

9. Structural vs. conceptual interoperability

Ossie currently achieves structural interoperability: different systems can read and write the same objects (datasets, fields, relationships, metrics). However, identical structures may use different names for the same business concept, preventing true conceptual interoperability. The roadmap plans an ontology layer to define canonical concepts such as Customer, Order, Product and map them to datasets and fields.

10. Missing query and execution semantics

Beyond structural exchange, a full cross‑platform semantic standard requires unified metric granularity, aggregation methods, filter rules, relationship cardinality, time dimensions, cumulative and derived metrics, as well as a standard query language and reference compiler to SQL. The roadmap lists these as future work, acknowledging that the current core spec (0.2.0.dev0) lacks these capabilities.

11. Conclusion

Apache Ossie provides an initial skeleton for exchanging semantic models, moving business semantics out of proprietary BI tools toward an open, vendor‑neutral standard. It currently delivers structural model exchange and AI‑context metadata, while conceptual interoperability, complex metric semantics and execution semantics remain work in progress.