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-e-component-execution-loop
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title: E-component (Execution Loop)
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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: [Execution Loop, E-Loop, Agent Runtime Loop]
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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: [agent, runtime, llm, architecture]
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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: anthropic-sdk
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---
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# E-component (Execution Loop)
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## 매 한 줄
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> **"매 LLM agent 의 heartbeat"**. 매 EST (Execution / State / Tools) component triad 의 E — 매 model-call → 매 tool-dispatch → 매 result-feedback 의 inner loop. 매 Claude Agent SDK / OpenAI Assistants / LangGraph 매 same primitive.
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## 매 핵심
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### 매 mechanism
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1. Send messages + tool definitions to LLM.
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2. LLM 매 returns text + (optional) tool_use blocks.
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3. If tool_use: dispatch to T-component (Tool Registry), append tool_result to state.
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4. Loop until 매 stop_reason == "end_turn" or max_iterations.
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### 매 components
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- **E (this)**: orchestrator — message-pump.
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- **S** (State Store): conversation history, scratch state.
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- **T** (Tool Registry): handler dispatch, schema validation.
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### 매 응용
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1. Code agents (Claude Code, Cursor, Devin).
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2. Research agents (Perplexity, Deep Research).
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3. Workflow automation.
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## 💻 패턴
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### Minimal execution loop (Anthropic SDK 2026)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def run(messages, tools, dispatch, max_iters=20):
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for _ in range(max_iters):
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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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tools=tools,
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messages=messages,
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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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tool_results = [
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{"type": "tool_result", "tool_use_id": b.id, "content": dispatch(b.name, b.input)}
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for b in resp.content if b.type == "tool_use"
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]
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messages.append({"role": "user", "content": tool_results})
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raise RuntimeError("max iterations exceeded")
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```
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### Streaming variant
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```python
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with client.messages.stream(model="claude-opus-4-7", messages=messages, tools=tools) as stream:
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for event in stream:
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if event.type == "content_block_delta":
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print(event.delta.text, end="", flush=True)
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final = stream.get_final_message()
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```
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### Tool dispatch (T-component plug-in)
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```python
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TOOLS = {
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"read_file": lambda input: open(input["path"]).read(),
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"list_dir": lambda input: os.listdir(input["path"]),
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}
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def dispatch(name, input):
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try: return TOOLS[name](input)
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except Exception as e: return f"Error: {e}"
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```
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### Stop conditions
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```python
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STOP = {"end_turn", "stop_sequence", "max_tokens"}
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if resp.stop_reason in STOP: break
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```
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### Prompt caching the system + tools
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```python
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resp = client.messages.create(
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model="claude-opus-4-7",
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system=[{"type": "text", "text": SYSTEM, "cache_control": {"type": "ephemeral"}}],
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tools=[{**t, "cache_control": {"type": "ephemeral"}} for t in tools],
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messages=messages,
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)
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```
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### Budget guard
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```python
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total_tokens = 0
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while True:
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resp = client.messages.create(...)
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total_tokens += resp.usage.input_tokens + resp.usage.output_tokens
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if total_tokens > BUDGET: raise BudgetExceeded()
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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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| Single-turn task | Direct API call |
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| Multi-tool task | E-loop |
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| Long-running workflow | E-loop + checkpointing (S) |
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**기본값**: E-loop + prompt caching + budget guard + max-iter clamp.
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## 🔗 Graph
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- 부모: [[Agent-Architecture]]
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- 변형: [[ReAct]]
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- 응용: [[LangGraph]]
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- Adjacent: [[S-component (State Store)]] · [[T-component (Tool Registry)]]
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## 🤖 LLM 활용
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**언제**: building agent runtime, multi-step tool-use task.
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**언제 X**: pure single-shot prompt (no tools, no state).
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## ❌ 안티패턴
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- **No max-iter cap**: 매 infinite-loop 의 risk.
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- **No budget guard**: 매 unbounded cost.
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- **Recreating system prompt per turn**: cache miss → 매 5-10x cost.
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## 🧪 검증 / 중복
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- Verified (Anthropic Messages API docs, Claude Agent SDK).
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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 — E-component FULL with SDK 2026 loop patterns |
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