Adoption hardening for internal teams

Problem

The YAML contract and normalizer were validated against a single pilot team (FT Analytics), but expanding to multiple internal teams and external design partners exposes edge cases, undocumented constraints, and fragile assumptions in the compiler and normalization passes. Without hardening — stricter validation, better error messages, consistent handling of edge-case YAML patterns — each new team will hit the same class of issues independently, creating a support burden that scales linearly with adoption.

Context

• Pilot work proved the baseline flow for the YAML contract, compiler/normalizer, execution adapters, and release/versioning, but repeated use by multiple internal teams and first design partners will surface reliability and UX edge cases that the pilot could work around manually.
• This milestone is about turning a promising path into something teams can use predictably without bespoke engineering help, support heroics, or undocumented workarounds.
• Expected touchpoints include dataface/core/, schema/compiled types, docs, and core test suites, plus any onboarding, validation, and issue-triage surfaces that repeatedly fail during broader adoption.

Possible Solutions

• A - Fix only the currently visible blockers: fast, but it tends to preserve hidden fragility and leaves the next round of adopters to rediscover the same classes of issues.
• B - Recommended: define an adoption-hardening checklist and close the highest-impact gaps: combine real usage review, targeted fixes, docs/runbook updates, and validation so repeated use becomes predictable.
• C - Pause broader adoption until a larger redesign is finished: reduces short-term support load, but delays the real feedback this milestone is meant to capture.

Plan

1. Review prototype/pilot outputs, open issues, and support friction to identify the top blockers to repeated team adoption of the YAML contract, compiler/normalizer, execution adapters, and release/versioning.
2. Turn those findings into a prioritized hardening scope that covers product behavior, docs/runbooks, and validation for the highest-risk flows.
3. Land or queue the highest-impact fixes and document explicit known limits for anything intentionally left out of scope.
4. Re-run representative multi-team or design-partner scenarios and update follow-up tasks based on what still fails or feels too brittle.

Implementation Progress

Review Feedback

• [ ] Review cleared