Quality and performance improvements

Problem

As usage scales, the context catalog's enrichment pipeline will face both quality and performance pressure — profiling queries that are too slow for large tables, enrichment rules that produce inaccurate metadata for edge-case schemas, and token-inefficient context formatting that wastes AI agent budget. Without targeted improvements driven by measured user-facing outcomes (query accuracy, agent success rate, profiling latency), optimization efforts will be unfocused and improvements will not translate to visible user benefit.

Context

• Once context schema/catalog contracts and Nimble enrichment flows across product surfaces is in regular use, quality and performance work needs to target the actual slow, flaky, or costly paths rather than generic optimization ideas.
• The right scope here is evidence-driven: identify bottlenecks, remove the highest-friction issues, and make sure the fixes are measurable and regression-resistant.
• Expected touchpoints include dataface/ai/, context-contract docs, eval wiring, and inspect-derived artifacts, telemetry or QA evidence, and any heavy workflows where users are paying the cost today.

Possible Solutions

• A - Tune isolated hotspots as they are reported: useful for emergencies, but it rarely produces a coherent quality/performance program.
• B - Recommended: prioritize measurable bottlenecks and quality gaps: couple performance work with correctness and UX validation so improvements are both faster and safer.
• C - Rewrite broad subsystems for theoretical speedups: tempting, but usually too risky and poorly grounded for this milestone.

Plan

1. Identify the biggest quality and performance pain points in context schema/catalog contracts and Nimble enrichment flows across product surfaces using real usage data, QA findings, and support feedback.
2. Choose a small set of improvements with clear before/after measures and explicit user-facing benefit.
3. Implement the fixes together with regression checks, docs, or operator notes wherever the change affects behavior or expectations.
4. Review the measured outcome and turn any remaining hotspots into sequenced follow-up tasks instead of leaving them as vague future work.

Implementation Progress

Review Feedback

• [ ] Review cleared