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-multi-agent-system
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title: Multi-agent System
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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: [MAS, 멀티에이전트, Agent Swarm, Agentic Systems]
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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: [ai, agents, llm, orchestration, distributed]
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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: python
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framework: claude-agent-sdk
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---
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# Multi-agent System
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## 매 한 줄
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> **"매 specialization × coordination > monolith"**. Multi-agent system 은 여러 autonomous agent 가 message passing / shared state 로 협업해 single agent 보다 큰 task 해결. 2026 LLM 시대에 Claude Agent SDK, OpenAI Swarm, LangGraph, AutoGen 등이 표준 framework.
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## 매 핵심
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### 매 architecture pattern
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- **Orchestrator-worker**: 1 lead agent + N specialist worker. 매 Anthropic 의 research agent 패턴.
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- **Peer-to-peer**: 모든 agent equal, message bus 로 통신.
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- **Hierarchical**: layered supervisor tree.
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- **Blackboard**: shared memory 기반 indirect coordination.
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### 매 communication
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- Function calling / tool use.
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- Structured message (JSON schema).
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- Shared filesystem / vector DB.
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- A2A (Agent-to-Agent) protocol (2025 Anthropic spec).
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### 매 응용
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1. Research / report generation (parallel search + synthesis).
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2. Software engineering (planner + coder + tester).
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3. Customer support routing.
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4. Game NPC behavior.
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## 💻 패턴
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### Orchestrator-worker (Claude Agent SDK)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def spawn_worker(task: str, system: str) -> str:
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=4096,
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system=system,
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messages=[{"role": "user", "content": task}],
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)
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return resp.content[0].text
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def orchestrate(query: str):
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plan = spawn_worker(
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f"Decompose into 3 sub-tasks: {query}",
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"You are a research planner. Output JSON list.",
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)
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subtasks = parse_plan(plan)
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results = [spawn_worker(t, "You are a domain expert.") for t in subtasks]
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return spawn_worker(
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f"Synthesize: {results}",
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"You are an editor. Merge into a coherent report.",
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)
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```
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### Tool-use loop
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```python
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def agent_loop(messages, tools, max_iter=10):
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for _ in range(max_iter):
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resp = client.messages.create(
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model="claude-opus-4-7",
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tools=tools,
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messages=messages,
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max_tokens=4096,
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)
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messages.append({"role": "assistant", "content": resp.content})
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if resp.stop_reason == "end_turn":
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return resp
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for block in resp.content:
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if block.type == "tool_use":
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result = execute_tool(block.name, block.input)
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messages.append({
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"role": "user",
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"content": [{
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"type": "tool_result",
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"tool_use_id": block.id,
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"content": result,
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}],
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})
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```
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### Shared state via filesystem
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```python
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import json, fcntl
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from pathlib import Path
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def shared_write(path: Path, key: str, value):
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with open(path, "r+") as f:
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fcntl.flock(f, fcntl.LOCK_EX)
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state = json.load(f)
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state[key] = value
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f.seek(0); f.truncate()
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json.dump(state, f)
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fcntl.flock(f, fcntl.LOCK_UN)
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```
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### LangGraph state machine
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```python
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from langgraph.graph import StateGraph, END
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def planner(state): return {"plan": llm_plan(state["query"])}
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def executor(state): return {"result": run_steps(state["plan"])}
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def critic(state):
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if quality_score(state["result"]) < 0.7:
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return {"next": "planner"}
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return {"next": END}
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g = StateGraph(dict)
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g.add_node("plan", planner)
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g.add_node("exec", executor)
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g.add_node("crit", critic)
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g.add_edge("plan", "exec")
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g.add_edge("exec", "crit")
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g.add_conditional_edges("crit", lambda s: s["next"])
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```
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### Parallel agent fan-out
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```python
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import asyncio
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async def parallel_search(queries: list[str]) -> list[str]:
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tasks = [asyncio.to_thread(spawn_worker, q, "Researcher") for q in queries]
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return await asyncio.gather(*tasks)
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```
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### Critic-actor consensus
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```python
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def consensus(question: str, n_agents=3) -> str:
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answers = [spawn_worker(question, f"Expert #{i}") for i in range(n_agents)]
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return spawn_worker(
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f"Q: {question}\nAnswers:\n" + "\n".join(answers) +
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"\nReturn consensus + dissent.",
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"You are a meta-reviewer.",
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)
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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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| Clear task decomposition | Orchestrator-worker |
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| Open-ended exploration | Peer-to-peer + blackboard |
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| Quality-critical | Critic-actor + consensus |
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| Latency-critical | Parallel fan-out |
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| Stateful workflow | LangGraph / state machine |
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**기본값**: Orchestrator-worker + tool use loop.
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## 🔗 Graph
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- 부모: [[Distributed Systems]]
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- 변형: [[Agent Orchestration]] · [[Swarm_Intelligence|Swarm Intelligence]]
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- 응용: [[LangGraph]]
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- Adjacent: [[Tool Use]] · [[Function Calling]]
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## 🤖 LLM 활용
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**언제**: complex task decomposition, parallel research, multi-step pipeline.
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**언제 X**: simple single-shot Q&A — overhead 만 추가.
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## ❌ 안티패턴
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- **Over-decomposition**: 너무 많은 agent → coordination overhead 폭증.
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- **No termination condition**: infinite loop 위험.
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- **Shared mutable state without lock**: race condition.
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- **Tool sprawl**: 한 agent 에 50+ tools — selection 정확도 폭락.
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## 🧪 검증 / 중복
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- Verified (Anthropic Multi-agent Research 2024, OpenAI Swarm, LangGraph 0.3).
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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 — orchestrator/tool-use/LangGraph 패턴 |
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