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Antigravity Agent c24165b8bc refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

2026-07-11 11:05:56 +09:00

4.7 KiB

Raw Blame History

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

title

SRE

매 한 줄

"매 reliability 의 feature 의 — 매 first feature 의". SRE (Site Reliability Engineering) 의 Google-originated discipline 의 software engineering 의 ops 의 applying. 핵심: SLOs 의 define, error budgets 의 enforce, toil 의 eliminate, blameless postmortems.

매 핵심

매 SRE 의 핵심 의 concepts

SLI: 매 measurement (e.g., 200-OK rate over 5min).
SLO: 매 target (e.g., 99.9% over 28d rolling).
SLA: 매 customer contract (with $ penalty).
Error budget: 매 100% - SLO. 매 budget 의 burn 시 release freeze.

매 four golden signals (Google)

Latency, Traffic, Errors, Saturation.

매 응용

SLO-driven alerting (multi-window burn rate).
Toil budget (≤50% of SRE time).
Blameless postmortem culture.

💻 패턴

Prometheus SLO recording rules

groups:
  - name: slo.rules
    interval: 30s
    rules:
      - record: api:availability:ratio_rate5m
        expr: |
          sum(rate(http_requests_total{job="api",code!~"5.."}[5m]))
          / sum(rate(http_requests_total{job="api"}[5m]))
      - record: api:availability:ratio_rate1h
        expr: |
          sum(rate(http_requests_total{job="api",code!~"5.."}[1h]))
          / sum(rate(http_requests_total{job="api"}[1h]))

Multi-window multi-burn-rate alert

- alert: ApiErrorBudgetFastBurn
  expr: |
    (1 - api:availability:ratio_rate5m) > (14.4 * 0.001)
    and
    (1 - api:availability:ratio_rate1h) > (14.4 * 0.001)
  for: 2m
  labels: { severity: page }
  annotations:
    summary: "Fast burn — 매 2% budget 의 1h 의 consume 의"

OpenTelemetry instrumentation (Node)

import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';

new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: process.env.OTEL_ENDPOINT }),
  instrumentations: [getNodeAutoInstrumentations()],
}).start();

Runbook automation (Python)

import kubernetes.client as k8s
def remediate(pod_name: str, ns: str):
    api = k8s.CoreV1Api()
    api.delete_namespaced_pod(pod_name, ns)
    notify_slack(f"매 auto-restart {ns}/{pod_name} (high mem)")

Postmortem template

# Incident YYYY-MM-DD: <title>
**Status**: resolved
**Impact**: <users affected, $ lost, duration>
**Severity**: SEV-2

## Timeline (UTC)
- 14:02 alert fired
- 14:05 oncall paged
- 14:18 root cause identified
- 14:31 mitigated

## Root Cause
<technical>

## Action Items
- [ ] (P0) Fix race in checkout-svc — owner: @x
- [ ] (P1) Add SLO alert for queue depth — owner: @y

Toil tracking

type Toil = { repetitive: boolean; manual: boolean; automatable: boolean; ts: Date };
// dashboard: toil hours / total hours per quarter, target ≤50%

매 결정 기준

상황	SLO
user-facing read API	99.9% availability, p99 <300ms
user-facing write API	99.95% availability, p99 <500ms
internal batch	99.5% job completion within window
free-tier feature	99% (lower budget = ship faster)

기본값: 99.9% availability, multi-burn-rate alerts, weekly error-budget review.

🔗 Graph

부모: DevOps · Production Engineering
변형: Platform Engineering · CI/CD Pipeline & IDE Security Integration
응용: Observability · Chaos Engineering
Adjacent: Prometheus · OpenTelemetry

🤖 LLM 활용

언제: postmortem drafting from timeline, log anomaly summarization, runbook generation, oncall question answering. 언제 X: auto-remediation 의 LLM-only — 매 hallucinated kubectl 의 prod 의 destroy.

❌ 안티패턴

No SLO: 매 alert noise — 매 every blip 의 page.
100% uptime goal: 매 unattainable, 매 budget 0 = no innovation.
Blame culture: postmortem 의 finger-pointing — engineers 의 hide incidents.
Toil unbounded: SREs 의 burned out — quit within 12mo.

🧪 검증 / 중복

Verified (Google SRE Book, SRE Workbook, Prometheus docs, Sloth SLO generator).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — SLO + burn-rate + OTel patterns

4.7 KiB Raw Blame History

SRE