v1.0 stability and defect burn-down

Problem

After public launch, the MCP server will accumulate defects from diverse real-world usage patterns that internal testing did not cover. Without a structured stability program — recurring triage, defect categorization, burn-down tracking, and reliability trend metrics — bugs will pile up without prioritization, regressions will go unnoticed, and the team will lose visibility into whether the system is improving or degrading. The MCP server's reliability directly affects agent task success rates; untracked instability erodes user trust and makes it impossible to set or meet SLAs for design partners.

Context

• After launch, recurring defects in AI agent tool interfaces, execution workflows, and eval-driven behavior tuning will damage trust faster than new features can restore it, so this phase should prioritize stability over new scope.
• The goal is to identify the repeat offenders, remove the highest support burden, and make failure patterns measurable enough that the team knows whether quality is improving.
• Expected touchpoints include dataface/ai/, MCP/tool contracts, cloud chat surfaces, eval runners, and prompt artifacts, bug history, support or incident notes, and any tests or QA gaps that let defects recur.

Possible Solutions

• A - Keep mixing bug fixes with feature work opportunistically: preserves flexibility, but lets long-tail reliability work stay perpetually unfinished.
• B - Recommended: run an explicit stability program: rank defect classes, burn down the highest-frequency issues, and pair fixes with validation so regressions stop recurring.
• C - Freeze all new work until zero known defects remain: simple in principle, but unrealistic and usually counterproductive.

Plan

1. Aggregate the recurring failures in AI agent tool interfaces, execution workflows, and eval-driven behavior tuning from bugs, support notes, and recent releases, then rank them by user impact and repeat rate.
2. Turn the top defect classes into a concrete burn-down list with owners, acceptance criteria, and the validation needed to keep each fix from regressing.
3. Land or schedule the highest-leverage fixes first, including any docs or operator changes that reduce repeat incidents.
4. Review the remaining defect mix after the first burn-down pass and update the next tranche of work based on actual stability improvements.

Implementation Progress

Review Feedback

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