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>
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Antigravity Agent
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---
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id: wiki-2026-0508-solution
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title: Solution
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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: [Solutioning, Solution Design, Problem-Solution Mapping]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [methodology, design-thinking, problem-solving, solutioning]
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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: methodology
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framework: design-thinking
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---
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# Solution
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## 매 한 줄
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> **"매 solution 의 problem 의 inverse 의 X — 매 fit 의 search"**. 매 problem statement → constraints → option-space → trade-off → committed solution. 매 design thinking + engineering rigor 의 fusion. 매 2026 modern PRD/RFC stack 매 LLM-aided option exploration 의 기본.
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## 매 핵심
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### 매 problem-vs-solution 의 분리
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- **Problem**: 매 user pain, business gap, technical debt. 매 solution-agnostic description.
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- **Solution**: 매 specific approach 의 implement. 매 multiple options 의 enumerate.
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- **Anti-pattern**: 매 "we need X" framing — 매 solution 의 jumped-to. 매 problem 의 first articulate.
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### 매 5-step canonical flow
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1. **Articulate**: 매 problem 의 1-sentence + 매 measurable success criterion.
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2. **Constrain**: 매 budget, deadline, team, tech-stack, risk tolerance.
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3. **Enumerate**: 매 3+ options. 매 do-nothing baseline 의 always include.
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4. **Trade-off**: 매 each option 의 cost/risk/value 의 score.
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5. **Commit + reverse-doc**: 매 chosen option 의 RFC/ADR write. 매 rejected options 의 reason 도 기록.
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### 매 응용
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1. Tech RFC / ADR.
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2. Product PRD.
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3. Customer-discovery loop.
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4. LLM-aided option generation.
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## 💻 패턴
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### ADR template (Markdown)
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```markdown
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# ADR-042: Switch to event-driven order pipeline
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## Status
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Accepted (2026-04-12)
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## Context
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Sync API call chain causes 2.3s p95 latency under peak load (12k rps).
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Current monolith RPC stack cannot scale beyond 18k rps without sharding.
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## Decision
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Adopt Kafka-based event pipeline for order lifecycle (created → paid → shipped).
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## Consequences
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+ p95 drops to 400ms (validated in load test).
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+ Decoupled services enable independent deploys.
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- Operational complexity: Kafka cluster, schema registry, DLQ.
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- 6-week migration with dual-write phase.
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## Alternatives considered
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1. Sharded monolith — rejected: 4mo migration, no future-proof.
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2. gRPC streaming — rejected: still tightly coupled.
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3. Do nothing — rejected: SLO breach by Q3.
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```
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### Option matrix scoring
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```python
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# option_matrix.py
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from dataclasses import dataclass
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@dataclass
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class Option:
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name: str
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value: int # 1-5 (impact)
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cost: int # 1-5 (effort)
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risk: int # 1-5 (uncertainty)
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@property
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def score(self) -> float:
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# Weighted: value heavy, risk penalizing
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return (self.value * 2.0) - (self.cost * 0.8) - (self.risk * 1.2)
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options = [
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Option("event-pipeline", value=5, cost=4, risk=3),
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Option("sharded-monolith", value=3, cost=4, risk=2),
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Option("do-nothing", value=0, cost=0, risk=5),
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]
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ranked = sorted(options, key=lambda o: o.score, reverse=True)
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for o in ranked:
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print(f"{o.name}: {o.score:.2f}")
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```
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### Problem-statement template
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```markdown
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**Who**: Mid-market SaaS ops engineers (50-500 employee orgs).
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**What**: Cannot debug Kafka consumer lag without SSH-ing into broker.
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**Why-now**: Compliance requires audit trail + zero-trust env (no SSH).
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**Success**: 80% of lag incidents resolvable via dashboard alone within 2026 Q3.
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**Non-goal**: Replacing existing Kafka cluster.
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```
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### LLM-aided option enumeration
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```python
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# enumerate_options.py
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from anthropic import Anthropic
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client = Anthropic()
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def enumerate(problem: str, constraints: list[str]) -> list[dict]:
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2000,
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messages=[{
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"role": "user",
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"content": f"""Problem: {problem}
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Constraints: {chr(10).join(f'- {c}' for c in constraints)}
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Generate 4 distinct solution options. Include 1 'do-nothing' baseline
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and 1 'wildcard' creative option. For each: name, summary, est_cost (1-5), est_risk (1-5)."""
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}]
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)
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return parse_options(msg.content[0].text)
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```
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### Trade-off heuristic gate
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```python
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def should_pursue(option) -> bool:
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if option.risk >= 5:
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return False # too uncertain, prototype first
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if option.cost > option.value:
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return False # net-negative
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return option.score > 0
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```
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### Reverse-doc rejected options
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```markdown
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## Rejected: GraphQL federation
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**Why considered**: Frontend wants typed schema, backend wants composability.
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**Why rejected**: Federation gateway adds 80ms median latency.
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Team has 0 GraphQL prod experience. 4mo onboarding curve.
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**Revisit when**: Latency budget grows OR team gains GraphQL expert.
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Vague stakeholder ask | 매 problem statement 5W 의 force |
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| 2+ viable options | 매 ADR + matrix scoring |
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| Reversible decision | 매 ship-and-iterate (Type 2) |
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| Irreversible decision | 매 deep RFC + review (Type 1) |
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| Unknown unknowns | 매 spike/prototype 의 first |
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**기본값**: ADR + 3+ option enumeration + reverse-doc.
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## 🔗 Graph
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- 부모: [[Design Thinking]]
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- 변형: [[RFC-Process]] · [[ADR]]
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## 🤖 LLM 활용
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**언제**: 매 option enumeration brainstorm, 매 ADR draft, 매 problem-statement refinement, 매 reverse-doc generation.
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**언제 X**: 매 commit decision (human accountability), 매 stakeholder alignment (in-person needed).
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## ❌ 안티패턴
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- **Solution-first**: 매 "we need Kafka" 매 problem 의 articulate 없이.
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- **Single option**: 매 alternatives 0개. 매 confirmation bias.
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- **No baseline**: 매 do-nothing option 의 omit. 매 cost 의 hidden.
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- **No reverse-doc**: 매 rejected options 의 oral history. 매 future-team 의 repeat.
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- **Solution worship**: 매 framework 의 fetishize over outcomes.
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## 🧪 검증 / 중복
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- Verified (Lightweight ADR by Michael Nygard; Amazon "1-pager"; ThoughtWorks Tech Radar 2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full content (5-step flow + ADR/option-matrix patterns) |
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