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Project Open to Alpha Team

Trace-to-Regression-Test Pipeline — close the user-feedback loop with a 24-hour SLA

Build the production pipeline that converts user-flagged failures into regression tests against a 24-hour SLA. User clicks `/feedback` (per PRJ-11 if shipped, or wires into existing feedback channels). Engineer triages within the same business day. Failure becomes a regression test in the eval substrate within 24 hours. The pipeline is instrumented end-to-end — cycle-time visible, conversion-rate visible, gap analysis surfaced.
Project Overview
Discipline
software-engineer-backend · AI Systems — AI Engineer · ai-systems-engineer
Duration
2 weeks
Compensation
Your stated freelance rate
Surface
Engineering · Product.ai
Kernels
engineering · productai
Outcomes
chat-expert · dev-integrate · team-velocity
Tier
Consequential
Alpha Team
Open to alpha members who want to take this on
Tooling
Claude Code or Co-work

Why we want this done

Anthropic's April 23, 2026 postmortem documented three overlapping bugs that degraded Claude Code outputs for 50 days. Internal evals "did not initially reproduce the issues identified" despite the regression making it past multiple human and automated code reviews, unit tests, end-to-end tests, automated verification, and dogfooding. User /feedback commands were the only signal that successfully triggered investigation. This is canonical: closed-loop AI verification devolves to compound deception. External truth anchors are the only durable defense. But anchors without conversion infrastructure are decorative. A /feedback channel that produces no regression tests is a feel-good ritual. The anchor only works when the captured failures get actively converted into the eval substrate, fast enough that the substrate stays synced with production reality. The candidate ships that conversion infrastructure on one Product.ai surface and proves the SLA empirically.

Scope

  1. Survey existing user-feedback infrastructure (PRJ-11 if shipped, support channels, in-product feedback widgets)
  2. Pick one surface (Product.ai chat, Alloy, or SimplyCodes) and instrument the pipeline end-to-end
  3. Triage layer — Slack notification on flagged failure with structured context summary; engineer claims it
  4. Conversion layer — the engineer (or the candidate) authors a regression test against the failure mode, commits it, wires it into CI
  5. Telemetry layer — cycle-time per failure (flag → test merged), per-engineer load, per-failure-class distribution
  6. SLA enforcement — paged escalation if a failure sits unclaimed >12 hours; named owner per failure class
  7. One-page operating manual for the team — the ritual, the SLAs, the on-call rotation if applicable

What success looks like

  • Pipeline runs end-to-end on one surface with at least three real flagged failures during the trial
  • Median cycle time (flag → regression test merged) under 24 hours
  • The regression tests catch the original failure (deterministic — re-running the same trace fails the test before the fix)
  • Telemetry surface (Cortex dashboard or AIOS dashboard) shows cycle-time and conversion-rate live
  • Operating manual is one page; a stranger reading it understands the ritual without re-asking
  • An on-call rotation is named (even if it's "the candidate plus the trial team" for this trial)

References

references.md
Backend Engineering Phase 3 briefing axiom B2 (Internal Evals Lag User Feedback), B3 (Verification Pipeline Component Stack), VERDICT 2 (Verification Instrumentation 90-Day Sprint)
AI Engineering Phase 3 briefing axioms A5 (External Truth Anchors), D1 (Eval Gap Irreducible)
Anthropic April 23 2026 Claude Code postmortem
axioms/Engineering/AI Engineering Verification Capability Pedagogy and Sourcing (2026-04-27).md
Existing Product.ai feedback channels and any prior trace-capture instrumentation
PRJ-11 (External Truth Anchor /feedback) — if shipped, this project consumes its output

Constraints

  • Claude Code as primary substrate
  • 24-hour SLA is the deliverable target; if the candidate believes 48 is more realistic, they argue for it explicitly
  • Privacy-respecting trace capture (PII handling documented)
  • Self-hostable eval substrate (Phoenix or Langfuse or DeepEval) — no Braintrust or LangSmith
  • Determinism: the regression test must catch the original failure trace verbatim
  • IP separation: surface code is application-layer; methodology paths (aios-methods) are out of scope
Apply
01

Read the Codex (10 min)

The operating principles we work by. If they resonate, the rest of this will land. Open the Codex →

02

12-minute video screen

Hireflix, async. Questions are calibrated to this project specifically.

03

Chemistry call (30-60 min)

Direct call with the CEO. Strategic alignment and mutual fit. No problem-solving exercise.

04

Project begins within 2-3 weeks

1099 contractor agreement, NDA, paid at your stated rate. Day 1 in Santa Monica.

Alpha Team members can take this project without the screen-and-call sequence. Reach out via the Alpha Team channel.

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