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

1. Data can be read across platforms but semantics stay locked inside tools

Physical data structures describe tables and fields, but they cannot answer how those fields should be used business‑wise. Different tools may define the same concept—such as "revenue"—with varying formulas, leading to metric drift for BI and incorrect queries for AI agents.

2. Ossie is an intermediate semantic model format, not a new BI product

Apache Ossie describes itself as an open specification for exchanging semantic metadata among analysis, AI, and BI platforms. It standardises how semantic objects are expressed and shared, but does not implement model management, permission control, query generation, caching, or optimisation itself.

3. Core objects defined by the current specification

The top‑level semantic_model contains datasets, relationships, metrics and custom_extensions. Datasets point to physical sources, fields describe attributes, relationships link datasets, and metrics store aggregation expressions. Expressions can be provided in multiple dialects (ANSI SQL, Snowflake SQL, MDX, Tableau, Databricks, GoodData MAQL, BigQuery).

4. ai_context brings business language into the model

The ai_context field can appear at any level (model, dataset, relationship, field, metric) and holds instructions, synonyms and examples to guide AI agents in mapping natural‑language queries to the correct metric, e.g., mapping "sales" or "total revenue" to a unified total_revenue metric.

While it improves consistency, the article warns that ai_context alone cannot eliminate AI hallucinations; data quality, model choice, query planning and access control still affect results.

5. Joining the Apache community solves governance, not technical maturity

Moving to the Apache Incubator provides a neutral governance model with public mailing lists, GitHub collaboration, and merit‑based committer status. The project entered incubation on 19 June 2026 and reached incubating status on 22 June 2026, but this does not imply it is a mature top‑level Apache project.

Since the repository opened in November 2025, it has received over 100 commits and 35 merged pull requests from contributors at Snowflake, Salesforce, Databricks, dbt Labs, RelationalAI, GoodData, Honeydew and others, indicating broad community interest.

6. Current focus is structural interoperability, not conceptual interoperability

Ossie enables different systems to read and write the same structural objects (datasets, fields, relationships, metrics). However, identical structures may use different names for the same business concept, and the specification does not yet resolve this “concept‑level” mapping.

The roadmap plans a separate ontology layer to define canonical concepts such as Customer, Order, Product and map them to dataset fields.

7. Full cross‑platform semantic standards still lack query and execution semantics

Beyond structural exchange, true interoperability requires unified metric granularity, aggregation rules, filter semantics, relationship cardinality, time handling, derived and cumulative metrics, and a standard query language. The current 0.2.0.dev0 version does not yet provide these, and the project cautions against production use.

Future work includes a standard semantic query language, reference compilers to SQL, and consistent handling of joins, aggregations and filters.

Conclusion

Apache Ossie has built an initial skeleton for exchanging semantic models, moving the focus of open data standards from raw tables to business meaning. It is not yet a complete, production‑ready semantic layer runtime, but it offers a promising open‑standard foundation for BI tools, data catalogs and AI agents to share metrics, dimensions, relationships and context.