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Antigravity Agent
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id: wiki-2026-0508-axify
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title: Axify (Engineering Productivity Platform)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Axify, DORA dashboard, value stream mapping, engineering metrics, AI impact measurement]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.85
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verification_status: applied
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tags: [dora, devops-metrics, engineering-productivity, value-stream, ai-impact, axify, observability]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: SaaS
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framework: Axify Platform
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---
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# Axify
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## 📌 한 줄 통찰
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> **"매 engineering productivity 의 데이터화"**. 매 DORA + value stream + AI 도입 의 measure. 매 vanity metric (사용량) 의 X — 매 actual outcome (lead time, deploy freq) 의 track. 매 modern engineering leader 의 dashboard.
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## 📖 핵심
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### 매 product
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- 매 SaaS platform.
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- 매 engineering leader 의 target.
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- 매 DORA + VSM + AI impact.
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### DORA Metrics (4 key)
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1. **Deployment Frequency**: 매 production 의 deploy 빈도.
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2. **Lead Time for Changes**: 매 commit → prod 의 시간.
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3. **Mean Time to Recover (MTTR)**: 매 incident 의 회복.
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4. **Change Failure Rate**: 매 deploy 후 의 incident 비율.
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→ 매 Google "Accelerate" 책 의 5-year research.
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### DORA tier
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| Tier | Deploy freq | Lead time | MTTR | CFR |
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|---|---|---|---|---|
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| Elite | On-demand | <1 hr | <1 hr | 0-15% |
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| High | weekly-monthly | 1 day-1 week | <1 day | 16-30% |
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| Medium | monthly | 1 week-1 month | <1 day | 16-30% |
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| Low | <monthly | 1-6 months | >1 week | 16-30% |
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### Value Stream Mapping (VSM)
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- 매 idea → prod 의 entire flow.
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- 매 wait time vs work time.
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- 매 bottleneck identify.
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- 매 Kanban / Lean 의 origin.
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### Axify Intelligence
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- 매 LLM-powered analyst.
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- 매 generic LLM 가 X — 매 org 의 repo / pipeline / incident 의 학습.
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- 매 metric 변동 의 cause analysis.
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- 매 chatbot 의 query.
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### AI Impact 측정 (vs vanity metric)
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| Vanity (X) | Real (✓) |
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|---|---|
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| 매 PR 의 # | 매 PR cycle time |
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| 매 AI 의 사용량 | 매 first review time |
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| 매 suggestion 의 # | 매 acceptance rate |
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| 매 line 의 generated | 매 deploy frequency |
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| 매 활성 user | 매 lead time |
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### 매 integration
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- **VCS**: GitHub, GitLab, Bitbucket, Azure DevOps.
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- **CI/CD**: Jenkins, CircleCI, GitHub Actions.
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- **Issue**: Jira, Linear.
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- **Chat**: Slack, MS Teams.
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### 매 case study
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- **BDC**: 매 51% deploy speed 향상.
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- **Newforma**: 매 22× deploy 의 frequency.
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### 매 alternative
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- **LinearB**: similar.
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- **Faros AI**: data layer.
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- **Code Climate Velocity**: similar.
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- **Sleuth**: incident-focused.
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- **Pluralsight Flow**: open-source-friendly.
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## 💻 패턴
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### DORA collection (custom)
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```python
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from datetime import datetime, timedelta
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from github import Github
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g = Github('token')
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repo = g.get_repo('org/repo')
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def deployment_frequency(weeks=4):
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cutoff = datetime.now() - timedelta(weeks=weeks)
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deploys = [d for d in repo.get_deployments()
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if d.created_at > cutoff and d.environment == 'production']
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return len(deploys) / weeks # 매 deploys per week
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def lead_time(weeks=4):
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cutoff = datetime.now() - timedelta(weeks=weeks)
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deploys = [d for d in repo.get_deployments()
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if d.created_at > cutoff and d.environment == 'production']
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lead_times = []
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for d in deploys:
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commit = repo.get_commit(d.sha)
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lead_times.append((d.created_at - commit.commit.author.date).total_seconds() / 3600)
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return median(lead_times) # hours
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def mttr(weeks=4):
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cutoff = datetime.now() - timedelta(weeks=weeks)
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incidents = fetch_incidents(cutoff) # 매 PagerDuty / Sentry / etc.
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return median(i.resolved_at - i.started_at for i in incidents).total_seconds() / 3600
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def change_failure_rate(weeks=4):
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deploys = count_deploys(weeks)
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incidents = count_incidents(weeks, related_to_deploy=True)
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return incidents / deploys
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```
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### Value Stream visualization
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```python
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def value_stream_data(repo, weeks=4):
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cutoff = datetime.now() - timedelta(weeks=weeks)
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stages = {
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'idea_to_first_commit': [],
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'first_commit_to_pr': [],
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'pr_to_review': [],
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'review_to_merge': [],
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'merge_to_deploy': [],
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}
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for ticket in fetch_tickets(cutoff):
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commits = ticket.linked_commits()
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prs = ticket.linked_prs()
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deploys = ticket.linked_deploys()
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if commits and ticket.created_at:
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stages['idea_to_first_commit'].append(commits[0].time - ticket.created_at)
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# ... etc
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return {stage: median(times) for stage, times in stages.items()}
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```
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### AI Impact measurement
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```python
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def ai_review_impact(before_date, after_date):
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"""매 AI 의 도입 전 / 후 의 metric 비교."""
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metrics = ['lead_time', 'pr_cycle_time', 'first_review_time',
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'deploy_frequency', 'change_failure_rate']
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return {
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m: {
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'before': calculate(m, ref_date=before_date, window=4),
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'after': calculate(m, ref_date=after_date, window=4),
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}
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for m in metrics
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}
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# 매 statistical significance check
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def is_significant(before_samples, after_samples, alpha=0.05):
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from scipy import stats
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_, p = stats.ttest_ind(before_samples, after_samples)
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return p < alpha
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```
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### Slack notification (DORA)
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```python
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import slack_sdk
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def post_weekly_dora():
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metrics = {
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'deploy_freq': deployment_frequency(),
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'lead_time_hours': lead_time(),
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'mttr_hours': mttr(),
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'cfr_pct': change_failure_rate() * 100,
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}
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text = f"""*Weekly DORA*
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:rocket: Deploys: {metrics['deploy_freq']:.1f}/week
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:stopwatch: Lead time: {metrics['lead_time_hours']:.1f}h
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:wrench: MTTR: {metrics['mttr_hours']:.1f}h
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:fire: CFR: {metrics['cfr_pct']:.1f}%"""
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slack_client.chat_postMessage(channel='#engineering', text=text)
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```
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### Outcome > vanity check
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```python
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def is_real_improvement(metric_before, metric_after, vanity_metric_change):
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"""매 vanity 의 increase 가 매 real metric 의 improve?"""
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real_improvement = metric_after['lead_time'] < metric_before['lead_time']
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if vanity_metric_change > 0 and not real_improvement:
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return 'WARN: vanity-only — 매 사용량 ↑ 가, 매 lead time 의 변화 X'
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return 'OK' if real_improvement else 'NEUTRAL'
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```
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## 🤔 결정 기준
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| 상황 | Tool |
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|---|---|
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| Modern team | Axify / LinearB |
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| Open-source / DIY | Custom DORA script |
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| Incident-focused | Sleuth |
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| Data warehouse | Faros AI |
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| Manual | GitHub Insights |
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| Free tier | Pluralsight Flow |
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**기본값**: DORA + VSM + AI impact 의 monthly review.
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## 🔗 Graph
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- 부모: [[DevOps]] · [[SRE]]
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- 변형: [[DORA-Metrics]] · [[Value-Stream-Mapping]] · [[Engineering-Metrics]]
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- Adjacent: [[CI CD]] · [[Git Branching Strategies]] · [[Code-Review]] · [[Goodharts-Law]]
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## 🤖 LLM 활용
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**언제**: 매 engineering metrics 의 design. 매 AI tool 도입 의 ROI 의 measure. 매 productivity dashboard.
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**언제 X**: 매 individual surveillance (toxic). 매 single metric goal (Goodhart).
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## ❌ 안티패턴
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- **Vanity metric 만**: 매 사용량 ↑ 가, 매 outcome X.
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- **Individual measure**: 매 surveillance.
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- **Single metric goal**: 매 game.
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- **No baseline**: 매 before-after 비교 X.
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- **Real-time alert 의 noise**: 매 fatigue.
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- **No statistical sig**: 매 noise 의 trend mistake.
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## 🧪 검증 / 중복
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- Verified (DORA "Accelerate" book, Axify docs).
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- 신뢰도 B.
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- Related: [[Quality_Code_Review_Modern]] · [[Git Branching Strategies]] · [[Goodharts-Law]] · [[CI CD]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-04-18 | Auto-mapped |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — DORA + VSM + AI impact + 매 GitHub API DORA collection code |
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