obsidian-vault/work/projects/macos-cloud-autofix-plan-2026-10-15.md

---
title: 'macOS: cloud flow for automatically fixing and validating bugs and tests (Claude/Codex) — plan'
date: '2026-10-15'
status: proposal
tags:
  - ddg
  - macos
  - ai-agents
  - ci
  - bitrise
  - sentry
  - virfield
---
# macOS: cloud flow for automatically fixing and validating bugs and tests

Status: **proposal**, not implemented. Compiled on 2026-10-15 from: Asana (AI Agent Infrastructure / O-T), Obsidian (Virfield, Personal OS, macos-tests-worktrees-2026-10-14, ci-pipeline), `.github/workflows` and `.claude/skills` in apple-browsers, and Anthropic / OpenAI / Sentry / Bitrise documentation.

Legend: ✅ confirmed by a source, ⚠️ not verified / needs a spike.

---

## 1. What already exists (so we don't build it twice)

### 1.1 In Asana (company experience)

| What | Summary | Lesson for us |
|---|---|---|
| **Windows crash triage** ([1214714826035263](https://app.asana.com/1/137249556945/task/1214714826035263)) | A Prefect job pulls Sentry via MCP → Claude agent → daily stability report → Release DRI. ~$8/day (~$250–500/month). Started on Cursor self-hosted, then moved to Claude on a **GitHub-hosted Windows runner** that Prefect triggers as a GHA. A Prefect variable stores "memory" between runs, so cost drops day over day. | A working pattern: **pre-fetch in a trusted zone → artifact → agent on the runner**. The agent does not call Sentry itself. |
| Same task: developer attitude to auto-PRs | In April auto-PRs were met with hostility ("reviewing a PR costs more than checking the idea"). The fix: every finding is **validated with a unit/UI reproducer test**, and the report is root cause + direction + test. After that 2 AI PRs landed with no review comments, the team agreed to auto-PRs, and ~20 CPMN of degradation was prevented. | Do not start with auto-PRs. Trust ladder: report → proposal → draft PR. **No fix is proposed without a failing test.** |
| **Find an alternative to Cursor Cloud Agents** ([1216757783325568](https://app.asana.com/1/137249556945/project/72649045549333/task/1216757783325568), DRI Chris Clarke) | 3 workflow classes: analyze-and-report / **build-and-fix** / drive-browser. Interactive Claude runners for Android, SERP and generic work already exist (claude.ai/code). Linux/Windows self-hosted GHA runners are in progress ([1218221113915359](https://app.asana.com/1/137249556945/task/1218221113915359)). **macOS is the main gap**: Azure has no macOS, so there is no path to the internal network (dub, ClickHouse, Sentry). The "Provision Mac Machines" project ([1215557028847084](https://app.asana.com/1/137249556945/project/1211983163358654/task/1215557028847084), AWS EC2 Mac) is **ON HOLD**. | We are closing exactly this gap. We need to plug into the AI Agent Infrastructure project rather than run a parallel one. |
| Context-transfer PoC ([1217164276206833](https://app.asana.com/1/137249556945/task/1217164276206833)) | A GHA run is not resumable, so a ZIP artifact holds transcript + prompt + metadata, and a skill restores the context in a new chat. Proposed as a company-wide GHA. | Solves "a human wants to step in" without interactive sessions. |
| **Bitrise** ([1218829288858490](https://app.asana.com/1/137249556945/project/1219113011535833/task/1218829288858490)) | The Apple team already uses **Bitrise as the macOS runner provider for GHA** (GitHub macOS runners are too slow); GHA has a 360-minute cap. Bitrise RDE (remote dev environment: SSH/VNC, suspend/resume) is in trial: 1 concurrent session, and a Bitrise GitHub App bug with private repos (Bitrise is fixing it). | For automated runs use what already works (GHA + Bitrise). RDE is for interactive work. |
| **Security triage: Bitrise RDE** ([1219413380003457](https://app.asana.com/1/137249556945/project/1199892415909552/task/1219413380003457), reply 2026-10-14) | (AT1) Direct access from Bitrise to internal systems is **not viable** (all Bitrise address pools would have to be opened). (AT2) Use the RDE MCP with a local Claude as orchestrator; set `SSH_AUTH_SOCK` to empty (it forwards keys). (AT3) Do not auto-approve RDE tools in a session that also has the ClickHouse/Sentry MCP; anything coming back from RDE is untrusted. RDE sessions themselves are isolated. | **Hard design constraint:** an agent on a Bitrise machine cannot see Sentry/ClickHouse/dub. Data arrives as an artifact. |
| Bug Squisher / Duck.ai | SERP: `[🤖 Bug Squisher]` PRs in ddg. Duck.ai: an engineer manually runs `$auto fix this bug` (a pstack port) → a ready PR → review. Automation comes later, once reliability is proven. Artur (Chromium): the agent does not drive the browser directly but **writes a reproducible journey script** that is run on the base and fix commits, so the agent cannot "lie" in validation. | Reuse this trick for validation (see §5). Align with flux/pstack so we don't add a fifth parallel framework (see the overlap comment in [1213572946361338](https://app.asana.com/1/137249556945/project/1209667197557478/task/1213572946361338)). |
| Gate model (same discussion) | Consensus gates (kick-off, tech design, post-mortem) belong to humans; an unattended agent skips them. Review gates (privacy/security triage, ship review) are mandatory — they stack at the end, and corrections send the agent back into the loop. | Agent flow = draft PR + mandatory review gates, no consensus gates. |
| Windows release quality ([1215834564874913](https://app.asana.com/1/137249556945/task/1215834564874913)) | Windows is the pilot; **macOS follows**. Preview catches crashes, but they are not turned into bugs with an owner (a "conversion problem"). | Our flow is the "converter" crash → reproduced test → draft PR for macOS. |

⚠️ I found no separate Asana task about **Android cloud AI bug fixing** — only the mention that Claude runners are available for Android/SERP/generic. If there is a specific link, it needs reading.
⚠️ There is no "Sentry crash report → Claude cloud" flow for Apple either: today it is the manual `ddg-sentry-report` skill (the user invokes it, it writes to Asana).

### 1.2 In the apple-browsers repo

- `macos_pr_checks.yml` (unit + integration on `macos-26` / `macos-26-xlarge`, release build, retry), `macos_ui_tests.yml` (class × macOS 14/15/26 matrix, nightly 03:00 UTC, **`workflow_dispatch` with `single_test_class`**, `user_modes`, `workflow_call`), `macos_run_ui_test.yml`, `macos_build_notarized.yml` (UI tests need the notarized Review app), `danger.yml`, `conflict_watch.yml`, `.github/actions/bitrise-build-cache`, `process-test-results`, `asana-failed-pr-checks`.
- Skills: `ddg-diagnose-ci-failure` (JUnit XML from artifacts, classification, flaky vs deterministic, regression timeline) and `ddg-sentry-report`. Both are **user-initiated**: they touch Asana, so the "lethal trifecta" policy blocks other MCPs afterwards.
- `AGENTS.md`, `CLAUDE.md`, `REVIEW.md` already exist.
- `.xcode-version` = 26.4 (ci-pipeline note). The Virfield profile `uitest-27-v1` = macOS 27 / Xcode 27, so there is a **mismatch**; we need an image for 26.4.

### 1.3 Our local achievements (Obsidian)

**Virfield** (Tart + SSH nodes, Go, MCP with 18 tools: `vm_acquire/vm_lease/vm_release/vm_tunnel`, `image_*`):
- ✅ Disposable macOS VM per lease, pinned SSH, artifact export, cleanup; goldens for Xcode 15.2 / 13.4.1 / 27.0.
- ✅ Fixed the things DDG UI tests fail on: locked keychain (login + default, GUI and SSH contexts), 1920×1080, ASIF/Recovery, SSH banner timeout. A real `DownloadsUITests.testDownloadFinishesThenPopupIsShown` ran twice with 0 failures (Xcode 27.1 products → guest 27.0).
- ⚠️ Not accepted yet: Broker/Runner → the full apple-browsers UI suite; installed `aee89c1` ≠ source `9a740c0`; false `vm_drift` events; admission quirks (storage reserve); **pool = 2 VMs on one Mac**; GitHub CI deferred. It is a good **dev/acceptance sandbox**, not a production fleet.

**Personal OS / Executor** (3 attempts, 2026-04…10):
- Executor v1 (autonomous LLM orchestrator) → **incident** (wrote to Asana, task data leaked to Discord). Executor v2 (bash runner/analyzer + 5 worker prompts: queued / in_progress / fix_ci / fix_comments / pr_review; state machine `queued→in_progress→pr→complete/abandoned`; no-output watchdog 30 min, hard limit 3 h; lockfile; analyzer filters) was implemented, but the crons are paused.
- Cross-cutting lessons: (1) **the deterministic layer worked, the agentic one did not**; (2) an agent must not write to external systems or have an outbound channel carrying work data, and human-in-the-loop is a mandatory gate; (4) weak hardware cannot carry an agent stack; (7) the model provider is config, not architecture; Codex bwrap does not start in Docker; do not proxy subscription OAuth tokens.

**Test fixes of 2026-10-14** ([[work/projects/macos-tests-worktrees-2026-10-14]]) — a ready quality reference and eval set:
- AddressBarTests: root cause = `ensureObjectDeallocated(.interrupt)` sends SIGINT while an object is still alive; two causes (autorelease in `TestRunHelper`, strong `self` in an animation completion). Validation: **main 10/10 FAIL → fix 15/15 PASS**, 7/7 isolated, full Integration ×2.
- Fire Window video leak: **not reproduced**; outcome = 4 guard tests, no fix (validity check: an intentional leak makes the test fail). "Not reproduced" is a legitimate outcome.
- Environment observations: **display sleep** breaks dealloc checks (display-link animations stall), so the VM/runner must keep the display awake; `async` tests mask releases; network mocking (OHHTTPStubs) and isolation of user settings are required.

### 1.4 External (checked against documentation)

| Platform | What it does | Limitation for us |
|---|---|---|
| **`anthropics/claude-code-action@v1`** | Automation mode (`prompt`); triggers: schedule / dispatch / issue / PR; `claude_args` (`--max-turns`, `--allowedTools`, `--model`); skills from `.claude/skills`; auth: API key, OAuth, **OIDC federation (no long-lived secrets)**; by default creates a branch and the PR is created via a link (a human clicks). Commits made with `GITHUB_TOKEN` do not trigger CI, so a GitHub App/custom token is needed. | ⚠️ The docs say "runs on GitHub-hosted runners"; running on macOS/a Bitrise label is **not explicitly described** → spike. Nothing prevents using `claude -p` directly in a job. |
| **`openai/codex-action@v1`** | `codex exec` in a job; **Linux or macOS runner**; `sandbox`, `safety-strategy: drop-sudo`, `allow-users`. | No fix-CI examples (only PR review). |
| **Claude Code cloud (claude.ai/code, routines)** | Sessions on Anthropic VMs; routines: schedule / **API trigger (`POST /fire`, the `text` field = alert payload)** / GitHub events; Auto-fix PR (reacts to CI failures and comments). | Anthropic VMs do not build Xcode. Routine limits: 30 fires/hour per routine. `text` is wrapped as untrusted. |
| **Claude self-hosted environments** (public beta, Team/Enterprise) | A runner on **our** host; outbound-only; routines and claude.ai/code route to it; ephemeral runner (`--drain-grace-sec 0` → clean disk); sessions are **resumable from any device**. A runner locks to one owner. | ⚠️ A macOS runner is not described in the docs (recipes are Kubernetes/Compose); Chris Clarke: "supports macOS, but we don't have any provisioned". Ask Anthropic (the project has a similar subtask for Windows). |
| **Sentry Seer** | Automatic Issue Fix: ≥10 events, activity within 14 days, fixability score; levels: stop after RCA / solution / PR / **hand-off to Claude Agent** (or Cursor, Copilot); Seer **never merges**. They recommend starting with "Stop after Solution". | Does not build the macOS project; needs the GitHub integration + code mappings; privacy (crash data → an external agent) needs a privacy triage. |
| **Bitrise Build Hub (GHA)** | `runs-on: <bitrise-label>`, M4 Pro, Xcode within 24 h of release, on-demand ephemeral with the GitHub App, US/EU. | No explicit egress-control guarantees. |
| **Bitrise RDE** | SSH/VNC, suspend/resume, MCP server. | See the security triage above; trial = 1 session. |
| **Bitrise AI Build Fixer** | Analyzes a failed build → PR → rebuild (Pro/Enterprise). | ⚠️ Described for Bitrise CI; not confirmed for "Build Hub under GHA". Covers only build failures, not flaky tests or crashes. |
| **Tart / Orchard / Tartelet / Cilicon** | OSS ephemeral macOS VMs on Apple silicon for self-hosted GHA runners. | ⚠️ The news about Cirrus CI shutting down comes from a competitor's blog and is unverified; Tart is OSS and does not depend on it. |

---

## 2. Problem statement

**Goal:** given a signal (a test fails/flakes on main; a new crash in Sentry; a bug), produce a **draft PR with evidence** that the fix works (before: red N/N, after: green N/N, lint, required schemes build), or a **reasoned refusal** ("not reproduced", "needs a human"). A human reviews and merges.

**Non-goals:** auto-merge; the agent writing to Asana/chats; fixing features/UX without an owner; release branches in the first stage.

**Task classes (increasing complexity and risk):**
1. Flaky/failing unit & integration test (plain macOS, no secrets).
2. Failing UI test (needs the notarized Review build, UITestCase, OS matrix).
3. Crash from Sentry (needs a reproducer; data comes from the internal zone).
4. Bug from Asana (needs repro steps; highest prompt-injection risk and risk of architectural mistakes).

---

## 3. Target architecture (shared by all options)

```mermaid
flowchart LR
  subgraph Signals["Signals"]
    A["CI: JUnit from main / nightly runs"]
    B["Sentry: new in release, N+ events"]
    C["Asana bug / manual trigger"]
  end
  subgraph Trusted["Trusted zone - Linux, has dub and Sentry access"]
    D["Deterministic intake:<br/>fingerprint, dedupe, eligibility,<br/>budget, PII scrub"]
    E["signal.json + artifacts"]
  end
  subgraph Mac["macOS runner - ephemeral, no internal network"]
    F["Agent job: analyze, repro, fix, validate"]
    G["Evidence bundle:<br/>xcresult, before/after, summary.json"]
  end
  H["Draft PR ai/fix/fingerprint<br/>label ai-generated"]
  I["Existing PR CI:<br/>unit + integration + UI on Bitrise"]
  J["Human: review and merge"]
  K["Notifier: Mattermost / Asana comment<br/>deterministic, narrow token"]
  A --> D
  B --> D
  C --> D
  D --> E --> F --> G --> H --> I --> J
  H --> K
  I -->|"CI failure"| F
```

Principles (from the Executor lessons and the security triage):
1. **A deterministic skeleton, with the LLM only in a narrow worker step.** Queue, dedupe, budget, retries and notifications are code, not an agent.
2. **The agent gets no Asana, Sentry or ClickHouse access.** Input is files (`signal.json`); output is a branch `ai/fix/<fingerprint>` + `summary.json`.
3. **Write access only as a draft PR on an `ai/*` branch** (GitHub App; ruleset: only `ai/*` may be pushed, `main` is closed). Notifications and Asana comments are done by a separate deterministic step.
4. **Signing/notarization secrets are not in the agent job.** The agent job runs unit/integration without secrets; UI tests are launched by the **orchestrator** on the agent's branch through the existing `macos_run_ui_test` (like any human PR).
5. **Provider = config:** `agent: claude|codex`, one prompt/skills/schema (`AGENTS.md` ↔ `CLAUDE.md`).
6. Input is **untrusted** (stack traces, bug texts, PR comments): pass it as data, minimal `--allowedTools`, no write MCPs.
7. Everything is idempotent: one fingerprint = one branch/PR; a retry does not create duplicates.

---

## 4. Execution options (venue)

| | Description | Pros | Cons / risks | When |
|---|---|---|---|---|
| **A. GHA + Bitrise Build Hub + `claude -p` / action (recommended start)** | A workflow in apple-browsers: `dispatch/schedule` → Linux job (intake) → macOS job on a Bitrise label with the agent. | Already used for CI; nothing new to buy; ephemeral; artifacts and OIDC out of the box; same PR loop as humans; same Xcode as CI (key for reproducibility). | Atomic (no chat) → context-transfer ZIP; 360-min cap; no internal network (fine, data arrives as an artifact); runner egress is not controlled → in-VM sandbox (Seatbelt) and prompt hardening; ⚠️ claude-action on macOS is not documented → spike. | Phases 1–3. Classes 1, 2, 3. |
| **B. Self-hosted Tart runners (Virfield as the image base)** | Our own Mac minis + Tart + (Orchard/Tartelet/Cilicon) as GHA runners, an image with Xcode 26.4 from our image pipeline. | Full network control (egress allowlist → internal data possible); interactive access over an SSH tunnel; no per-minute cost. | Needs DDG hardware (the "Provision Mac" project is on hold; AWS EC2 Mac is expensive, with a 24 h dedicated-host minimum ⚠️); ops load; Virfield is not accepted and is currently a personal Mac with 2 VMs (bus factor 1). | Phase 4+, if A hits cost/network limits. Virfield stays a **dev/acceptance sandbox**. |
| **C. Claude self-hosted environment on Mac (Tart VM per session)** | An Anthropic runner on a macOS host; triggers: routines `POST /fire` (from Sentry/GHA), claude.ai/code. | **Resumable interactive sessions** from any device (closes A's main gap); routines/API trigger; ephemeral runner = clean disk. | Beta, Team/Enterprise, ⚠️ macOS unconfirmed; owner lock → needs a service account; privacy/security triage for Claude self-hosted is already marked "revisit"; depends on the Anthropic control plane. | Phase 4, after Anthropic answers. Interactive "human joins". |
| **D. Bitrise RDE + Claude** | A long-lived macOS VM with SSH/VNC and MCP. | Interactive, suspend/resume, screenshots/VNC. | Trial = 1 session, private-repo bug, no internal network, SSH agent forwarding (blank it), does not scale, orchestrator = local Claude. | One-off hard bugs / UI repro, not a pipeline. |
| **E. Cloud Linux agents + CI as the oracle** | Claude auto-fix PR / Seer hand-off / Codex cloud edit code without building; Bitrise CI validates. | Zero infrastructure; quick to start; good for lint/compile/obvious tests. | Cannot reproduce or debug flaky tests (no Xcode); every iteration is a full CI cycle (slow/expensive); auto-fix reacts to CI but does not replace a reproducer. | **Complement** to A: (1) Seer RCA as input, (2) auto-fix on already-open AI PRs. |
| **F. Bitrise AI Build Fixer** | A ready-made Bitrise feature. | Nothing to write. | ⚠️ Build failures only and probably Bitrise CI only; Pro/Enterprise; closed logic/validation. | Not a base. Check it if build breakages are frequent. |

**Recommendation:** **A as the base + E as a complement (Seer RCA / auto-fix)**, then choose **C or B** for interactivity and cost reduction based on data. Virfield is for developing/accepting images and prompts locally, so we don't burn Bitrise minutes on debugging.

### Claude vs Codex
- One harness, two backends: `scripts/ai-fix/run-agent.sh --agent claude|codex`. Same input/output (`signal.json` → `summary.json` + branch).
- Claude first (corporate keys/plugins, `ddg-required-config`, OIDC federation, our skills). Codex as an A/B comparison on the same eval set and insurance against a provider change. The Codex action supports a macOS runner and `drop-sudo`; Codex cloud (a Linux container) is not suitable for Xcode ⚠️ (the docs are silent about macOS).
- Compare by the §8 metrics on identical tasks, not by feel.

---

## 5. Validation (the core of the plan)

### 5.1 Ladder by signal type

| Type | Repro | A fix counts as valid if |
|---|---|---|
| Flaky unit/integration | `-test-iterations N` / `-run-tests-until-failure` on the same Xcode/OS, **display awake** (otherwise false dealloc flakes, see 10/14). Start from CI history (failure rate). | Red before (≥K of N), green **M/M** after (M ≥ 15–20); not masked (`async`); whole class + neighbours green; network/settings mocked; `swiftlint --strict` 0; schemes `macOS Browser` and `macOS Browser App Store` build. |
| Failing test on main | bisect against the last-green CI sha | Is the cause the test or the product? If the product — fix + test; afterwards the full unit suite. |
| Failing UI test | run `single_test_class` on the notarized Review build through the existing workflow | Green on the target OS; then the 14/15/26 matrix on request; `-retry-tests-on-failure` does not count as "green". |
| Crash (Sentry) | **a mandatory reproducer** (unit/UI/stress test). No reproducer → RCA + hypothesis only, no PR. | The test is red before the fix and green after; the symptom matches the stack trace; no new crashes in neighbouring tests. |
| Bug (Asana) | a journey script written by the agent | The same script run on base and on fix shows a difference; acceptance criteria from the ticket; a human confirms the direction before the PR (two-step mode). |

### 5.2 Mandatory evidence bundle
`summary.json` by schema: `signal_id`, `classification`, `root_cause`, `confidence`, `repro: {command, before: k/n fail, after: m/n pass}`, `validation: {schemes, lint, test_classes}`, `files_changed`, `outcome: fix | guard_tests_only | not_reproduced | needs_human`, `risks`, `commands_run`, plus `.xcresult`/logs. The PR description is generated deterministically from this file.

### 5.3 Protection against "the agent lies"
- Validation is done by a **script**, not by the LLM: the agent supplies a command/test, the harness runs it N times and counts itself.
- Artur's trick: **a single journey script for base and fix**.
- Mutation check (as on 10/14): deliberately reintroduce the bug → the test must fail.
- A second agent in a clean context as reviewer per `REVIEW.md` (`/code-review`); the result is a PR comment, not a blocker.
- **Diff limits:** file/line caps; changes to test infrastructure/assertions without reason → `needs_human`; weakening assertions and deleting tests are forbidden (as in the `macos-uitest` skill).

### 5.4 Eval set before enabling automation
Golden cases (known "correct" fix): the AddressBarTests flake (root cause in 2 places), the Ask AI Chat button animation leak, the DeallocationTests display-sleep crash, the Fire Window leak (**expected outcome = not_reproduced**), plus 5–10 historical fixes from `git log` / the Asana "Apple CI Failing Tests" project. Metrics: was the root cause found, is the fix valid, **was a fix not invented where none exists**. The threshold for enabling the schedule is agreed in advance.

---

## 6. Security and privacy

Threats and mitigations:
- **Prompt injection** via Sentry/Asana/PR comments → inputs as data; minimal `--allowedTools`; no write MCPs; `--max-turns` budget; network denied except the allowlist.
- **Exfiltration:** the agent has no application secrets; the GitHub App token is narrow (`ai/*`); OIDC instead of static keys; **internal data only in `signal.json` after scrubbing** (user data, URLs, PII) in a trusted Linux job; for the Windows version a privacy follow-up on blob transfer is already open, so open a similar triage for Apple.
- **Egress on Bitrise runners** is not controlled → in-VM **Seatbelt sandbox** (Claude Code / Codex on macOS) + proxy allowlist (github.com, api.anthropic.com / api.openai.com, required registries/CDNs); in option B — pf on the host.
- **SSH forwarding** (from the triage): do not forward `SSH_AUTH_SOCK`; in the RDE option — per AT2/AT3.
- **Signing:** build-with-secrets and agent are separate jobs; UI tests on an `ai/*` branch go the same way as a human PR (ruleset + environment approvals for secrets).
- **Kill switch:** `vars.AI_FIX_ENABLED`, a daily $ budget, `maxActiveRuns`, an allowlist of classes/paths (by default do not touch: signing, entitlements, `Package.swift` of external dependencies, release workflows, `.github/**`).
- **Audit:** transcript + commands kept as an artifact for 30 days (like the Virfield journal).
- Review gates: privacy triage for changes (if a PR touches pixels/privacy — Danger/`privacy-review.yml` already exist); the PR template requires an Asana link → **the Asana task is created/linked by a human or a deterministic service**, not by the agent.
- Do not repeat: an autonomous LLM orchestrator; writes to Asana/Discord from the agent; proxying subscription OAuth tokens.

Approvals needed (in parallel with Phase 0): privacy + security triage for (a) a Bitrise runner + Anthropic/OpenAI API with apple-browsers code, (b) passing scrubbed Sentry data, (c) Claude self-hosted (if C).

---

## 7. Phased plan

### Phase 0 — alignment (≈1 week, 2–3 person-days)
- Join "AI Agent Infrastructure" ([project](https://app.asana.com/1/137249556945/project/1219113011535833)) and talk to Chris Clarke: record that apple-browsers = build-and-fix on macOS via GHA + Bitrise (what the comments already agree on), not a new stack.
- Sync with Artur/Sebastien/Tim (flux/pstack, verification) and Bárdi (Prefect crash flow, Windows runner) — take **their** harness/skills instead of writing our own; our contribution is the macOS specifics (Xcode, XCTest, display sleep, UITestCase).
- Open the privacy/security triage (§6). Order a dedicated API key (the form from the tickets) or OIDC federation.
- Baseline metrics: CI retry share, top-N flaky tests over 30 days (from JUnit), open "Apple CI Failing Tests".
- Result: a decision on venue A, a stakeholder list, agreed guardrails.

### Phase 1 — deterministic skeleton + report-only (≈2 weeks, ~8 person-days)
- Workflow `ai_triage_macos.yml` (schedule + dispatch): collects JUnit from recent main/nightly runs (`process-test-results`, `gh api` — like `ddg-diagnose-ci-failure`), computes failure rate, fingerprints, dedupe, eligibility; publishes a report (Mattermost/job summary). **No LLM writes.**
- `signal.json` / `summary.json` schemas; `run-agent.sh` (claude|codex), path allowlist, budget.
- The §5.4 eval set + harness locally on **Virfield** (cheap prompt debugging on an Xcode 26.4 image; build the image with `image_create` if needed).
- Spike S1 (claude/codex CLI on a Bitrise runner: install, auth, Seatbelt, cost per minute on a single test).
- Exit: a daily report; the eval harness runs; S1 gave time/cost numbers.

### Phase 2 — test agent: draft PR on request (≈3 weeks, ~12 person-days)
- `ai_fix_test.yml` (`workflow_dispatch`: `test_id`, `agent`, `mode`): Linux intake → macOS agent job (analyze → repro N times → fix → validate) → evidence bundle → draft PR `ai/fix/<fp>` with a label.
- A human triggers it manually (like `$auto` in Duck.ai). Then the existing PR CI; UI tests via a separate orchestrator dispatch.
- A/B Claude vs Codex on the eval set.
- PR-feedback loop: an `@ai-fix` comment or a repeat dispatch → same branch + context-transfer ZIP; optionally Claude auto-fix for CI failures on `ai/*` (option E).
- Exit: ≥ the threshold on the eval (root cause found, no false fixes); 5 real PRs passed review; average cost per fix measured.

### Phase 3 — crashes: Sentry → proposal → draft PR (≈3 weeks, ~12 person-days)
- Pre-fetch in a trusted Linux job (Sentry MCP/API; projects `apple-ios`, `apple-macos`; filters `firstRelease`, `app_version`, events ≥ N; reuse the `ddg-sentry-report` logic) → scrub → `signal.json`.
- In parallel a **Seer trial** on `apple-macos` in "Stop after Solution" mode (if privacy approves): use the RCA as an extra input/benchmark; compare with our agent.
- Two-step mode by default: proposal (root cause + reproducer test + direction) → comment to the owner → after a "yes" the agent makes the PR. After trust accumulates (as in the Windows case) — automatic draft PRs for crash classes with a reproduced test.
- Exit: ≥ X crash findings with a reproducer; share of directions accepted by owners.

### Phase 4 — automation and interactivity (≈4–6 weeks, depending on results)
- Enable the schedule (nightly top-K eligible), limits/budget, a metrics dashboard.
- Choose interactivity by data: **C** (if Anthropic confirms macOS), **B** (if Bitrise minutes cost/network become a problem), or keep D for rare cases.
- Asana bug fixes (class 4) only after proven reliability on 1–3 and together with flux/pstack.
- A decision on release branches is separate.

Rough estimate through Phase 3: ~**8–10 weeks elapsed, ~35–45 person-days** (rough; depends heavily on triage and the S1 results).

---

## 8. Metrics and stop criteria

- **Acceptance rate:** share of AI PRs merged without major rework.
- **Precision:** share of fixes that survived production without regression; share of `not_reproduced` outcomes that were actually real (false negatives).
- **Cost per merged fix** (tokens + Bitrise minutes), **time-to-PR**, **reviewer-minutes per PR**.
- **Flaky rate / CI retry rate** on main before/after; for crashes — the CPMN/crash-free delta for affected signatures.
- Stop conditions: acceptance below threshold two cycles in a row; any leak incident; budget exceeded → automatic kill switch.

---

## 9. Risks

| Risk | Mitigation |
|---|---|
| The agent fixes the symptom (weakens an assertion, adds a sleep/retry) | Prompt prohibition + diff policy + the harness counts N/N itself + mutation check |
| False flaky fixes (the test was broken, not the product) | "test bug vs product bug" classifier + review agent + owner |
| Display sleep/environment behaves differently from CI | Same image/Xcode, `caffeinate`/display awake, compare with CI history |
| Xcode mismatch (26.4 vs 27) | Image strictly per `.xcode-version`; a 26.4 Virfield profile |
| Bitrise minutes/token cost | Budgets, top-K, nightly mode, a Sonnet-class model by default, caching (Bitrise build cache action) |
| Prompt injection via Sentry/Asana | Scrub + data-only + minimal tools + sandbox |
| Duplicating others' efforts (flux/pstack/Prefect/Bug Squisher) | Phase 0, a shared harness, our contribution is the macOS layer |
| Uncontrolled PR growth | One fingerprint = one PR, a cap on open AI PRs, stale auto-close |
| Dependence on personal infrastructure (Virfield on one Mac) | Virfield is not on the production path |
| Anthropic beta features (self-hosted, routines `experimental` header) | Not on the critical path until GA |

---

## 10. Open questions (need decisions/data)

1. Confirm venue A: who is the DRI for macOS in AI Agent Infrastructure, and can a Bitrise runner label be allocated for agent jobs.
2. ⚠️ S1: do `claude -p` / claude-code-action work on a Bitrise macOS runner (auth, Seatbelt, network)? What does a minute/task cost?
3. ⚠️ Anthropic's answer: a macOS runner for self-hosted environments — timing and support.
4. Privacy/security: may scrubbed Sentry stack traces and apple-browsers code be sent to an agent on a Bitrise runner (OpenAI needs a separate approval).
5. Where the pipeline lives: apple-browsers (`.github/workflows`) vs a separate repo + a shared flux plugin.
6. Who owns the Asana linking of the PR (the template requires an Asana link): a deterministic service or a human.
7. Android: clarify whether a separate project exists (none found) — maybe the pattern can simply be reused.
8. Whether to run a pilot with "iOS/macOS unit flaky only" before crashes (I recommend yes).

---

## 11. First concrete steps (this week)

1. Write a short comment/task in AI Agent Infrastructure with this plan and questions 1–3 (without duplicating projects).
2. Assemble the eval set (4–5 cases from 10/14 + 5 from history) and describe the "expected outcome" for each.
3. Locally on Virfield (or a dev Mac) run `claude -p` on one case with the "N× before/after" harness — get a baseline of quality and cost before any CI integration.
4. Do spike S1 on a Bitrise runner in a separate branch (workflow_dispatch, a single test).
5. Prepare a draft privacy/security triage for (a) and (b).

Related: [[work/wiki/apple-browsers/ci-pipeline]], [[work/wiki/apple-browsers/vm-ui-testing]], [[personal/projects/virfield]], [[personal/projects/personal-os/executor-v2-redesign]], [[personal/projects/personal-os/executor-security-analysis]], [[personal/projects/personal-os/personal-os-failed-approaches]], [[work/projects/macos-tests-worktrees-2026-10-14]], [[Research/Isolation of Agent Execution - OS and Container Level]].