AI_CONTEXT profiling layers 1-5 foundation built

Problem

The context catalog had no automated profiling foundation — no schema introspection, statistical summaries, sample data, or semantic/quality inference. AI agents received only raw column names and types with no enrichment, making it difficult to assess data quality, understand value distributions, or infer column semantics. Every consumer of catalog metadata had to independently discover these properties, leading to duplicated effort and inconsistent understanding of the underlying data.

Context

• Layer 1-5 pipeline implementation is in place and used for context generation.
• Dialect hooks, statistical profiling, semantic detectors, and quality detectors are operational.
• Prototype evidence is available in code and docs for this completed baseline.

Possible Solutions

Plan

• Confirm completion evidence is linked in architecture docs and code references.
• Keep regression tests aligned as baseline behavior evolves.
• Use this task as dependency anchor for M1 AI_CONTEXT expansion work.

Implementation Progress

• Prototype baseline recorded as completed and linked to follow-on milestone work.

• Remaining scope is tracked in explicit M1+ tasks rather than implicit debt.

• AI Context Architecture current-status table

Review Feedback

• [ ] Review cleared