Apache Ossie is weighing a hybrid query design for semantic layers used by BI tools and AI agents, aiming to make answers more consistent rather than letting every system improvise its own interpretation. That matters because reliable data access is less about producing another fluent paragraph and more about ensuring the numbers mean the same thing each time.
Watch Desk analysis
What happened
A report published by Iceberg Lakehouse on 22 September says the Apache Ossie community is debating how agents and business-intelligence tools should query a semantic layer. The discussion centres on two competing approaches: a restricted query model with strict semantics and typed errors, or ordinary SQL extended with a MEASURE() aggregate.
The emerging direction is a layered design combining elements of both. In practice, that would preserve familiar SQL where it is useful while adding tighter rules for the business measures and semantic definitions that determine whether two apparently similar questions really have the same answer.
The source describes this as a community discussion, not a finished Apache Ossie release or a confirmed production standard. The proposed design and its eventual implementation could still change.
Why it matters
AI agents querying business data face a rather less glamorous problem than the usual demos suggest: a neatly phrased answer can still be wrong if the underlying metric is ambiguous. “Revenue”, “active customer” and “margin” may each have several legitimate definitions, depending on the organisation and reporting period.
A restricted model with typed errors could make those ambiguities visible instead of allowing an agent to quietly generate plausible SQL. Keeping a route for ordinary SQL, meanwhile, could make the system easier for existing BI teams to adopt. The trade-off is familiar technology fare: more guardrails can improve consistency, but too many can make a tool feel like it needs a solicitor before it will answer a question.
For readers building agent-powered analytics, the practical lesson is to inspect how a system represents measures, validates queries and reports uncertainty. Model fluency is not a substitute for shared definitions.
Our read
This is a useful design debate because it tackles the plumbing beneath AI analytics. The interesting choice is not simply SQL versus a new language. It is whether an agent should be allowed to guess when a business question is underspecified, or required to surface the ambiguity for a person to resolve.
The hybrid direction sounds more workable than choosing purity over adoption, but the proof will be in the error messages, compatibility and behaviour on messy real-world schemas. A semantic layer that prevents confident nonsense would be a small but meaningful win.
What to watch
- Whether Apache Ossie publishes a concrete proposal or implementation details for the layered model.
- How strict semantics and typed errors are represented in actual queries.
- Whether existing SQL and BI tools can use the design without extensive rewrites.
- Independent examples showing that agents produce more consistent answers, rather than merely more constrained ones.
Discussion spark: Should AI agents be forced to stop when a business metric is ambiguous, or should they be allowed to make a clearly labelled best guess?
Sources and evidence
- How Apache Ossie Is Deciding What Agents and BI Tools Can Ask a Semantic Layer (22 September 2026, 01:17 UTC)
Watch Desk is operated by WittyWires as an independent cross-cutting AI news tracker. It does not speak for the organisations or people it covers.