Quality standards and guardrails

Problem

As more engineers contribute to the MCP server, agent prompts, and eval cases, there are no enforced quality standards. Eval cases vary in structure and coverage depth, prompt templates are inconsistent in tone and formatting, and tool response schemas have no validation beyond ad-hoc testing. Without codified standards — what constitutes a passing eval, how tool responses must be structured, what prompt patterns are approved — quality will degrade as the contributor base grows. The guardrail framework needs explicit rules so that new contributors cannot accidentally ship regressions or inconsistent behavior.

Context

• Teams are judging readiness for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning inconsistently because there is no single quality bar that covers correctness, UX clarity, failure handling, and maintenance expectations.
• Without explicit standards, work gets approved on local intuition and later re-opened when another reviewer finds a gap that was never written down.
• Expected touchpoints include dataface/ai/, MCP/tool contracts, cloud chat surfaces, eval runners, and prompt artifacts, review checklists, docs, and any eval or QA surfaces used to prove a change is safe to ship.

Possible Solutions

• A - Rely on experienced reviewers to enforce quality informally: flexible, but it does not scale and leaves decisions hard to reproduce.
• B - Recommended: define a concise quality rubric plus guardrails: specify acceptance criteria, required evidence, and clear anti-goals so reviews are consistent.
• C - Block all new work until a comprehensive handbook exists: safer in theory, but too heavy for the milestone and likely to stall momentum.

Plan

1. List the failure modes and review disagreements that matter most for AI agent tool interfaces, execution workflows, and eval-driven behavior tuning, using recent work as concrete examples.
2. Turn those into a small set of quality standards, required validation evidence, and explicit guardrails for unsupported or risky cases.
3. Update the relevant docs, task/checklist expectations, and test or QA hooks so the standards are actually enforced.
4. Use the rubric on a representative set of recent or in-flight items and tighten the wording anywhere it still leaves too much ambiguity.

Implementation Progress

Review Feedback

• [ ] Review cleared