Quality standards and guardrails¶

Problem¶

As more contributors add enrichment rules, metadata sources, and profiling layers to the context catalog, there are no enforced quality standards governing what constitutes acceptable output. Without guardrails, new contributions can introduce inconsistent field naming, unreliable enrichment logic, or metadata that degrades rather than improves AI agent performance. The lack of standards makes it impossible to maintain consistent context quality as the system scales beyond its original authors.

Quality standards and guardrails¶

Problem¶

Context¶

Possible Solutions¶

Plan¶

Implementation Progress¶

Review Feedback¶