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-real-time-operation
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title: Real-time Operation
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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: [Real-time Systems, RTOS, Real-time Inference]
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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: [real-time, latency, rtos, streaming, inference]
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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: vllm
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---
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# Real-time Operation
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## 매 한 줄
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> **"매 deadline 의 miss 의 failure"**. Real-time 의 fast 와 X — predictable latency budget 의 within. Hard RT (RTOS, avionics) 의 missed deadline 의 catastrophic; soft RT (video, LLM streaming) 의 degraded UX.
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## 매 핵심
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### 매 분류
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- **Hard RT**: 매 deadline 의 absolute (pacemaker, ABS brake). RTOS — VxWorks, QNX, Zephyr.
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- **Firm RT**: 매 occasional miss 의 OK but useless after deadline (live video frame).
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- **Soft RT**: 매 best-effort, degraded quality on miss (LLM token stream, web UI).
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### 매 Latency budgets
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- **HFT**: <10μs.
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- **Game frame (60fps)**: 16.6ms.
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- **VR frame (90fps)**: 11ms (motion-to-photon <20ms).
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- **Web TTI**: <200ms perceived instant.
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- **LLM TTFT**: <500ms (Claude Opus 4.7 streaming).
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- **LLM inter-token**: <50ms (20 tok/s minimum readable).
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### 매 Web real-time
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- **SSE**: 매 server-push, HTTP/1.1 + 2, simple. LLM streaming default.
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- **WebSocket**: bidirectional, binary OK. Chat, multiplayer.
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- **WebRTC**: 매 P2P, sub-100ms voice/video.
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- **HTTP/3 + WebTransport**: 매 2026 emerging — UDP-based, multiplexed.
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### 매 AI Real-time inference
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- **vLLM**: PagedAttention — 매 24x throughput vs naive.
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- **MLX (Apple Silicon)**: M3/M4 의 unified memory — Llama 3.x 70B 의 local realtime.
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- **Speculative decoding**: small draft model 의 2-3x speedup.
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- **KV cache**: 매 prefix sharing — system prompt 의 cache.
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- **Prompt caching (Anthropic)**: 매 90% cost cut, lower TTFT.
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### 매 응용
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1. LLM chat 의 streaming token-by-token.
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2. Video conferencing (WebRTC).
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3. Trading systems (kdb+, FPGA).
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4. Robotics control loop (ROS 2 + Zephyr).
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5. Live captioning (Whisper streaming).
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## 💻 패턴
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### LLM streaming with prompt cache
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```python
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from anthropic import Anthropic
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client = Anthropic()
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with client.messages.stream(
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model="claude-opus-4-7",
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max_tokens=2048,
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system=[{
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"type": "text",
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"text": LARGE_SYSTEM_PROMPT, # 10k+ tokens
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"cache_control": {"type": "ephemeral"},
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}],
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messages=[{"role": "user", "content": "..."}],
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) as stream:
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for text in stream.text_stream:
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print(text, end="", flush=True)
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```
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### SSE in FastAPI
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```python
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from fastapi import FastAPI
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from fastapi.responses import StreamingResponse
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import asyncio
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app = FastAPI()
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async def event_stream():
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for i in range(100):
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yield f"data: token {i}\n\n"
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await asyncio.sleep(0.05)
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@app.get("/stream")
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async def stream():
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return StreamingResponse(event_stream(), media_type="text/event-stream")
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```
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### vLLM batched inference server
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="meta-llama/Llama-3.3-70B", tensor_parallel_size=4,
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enable_prefix_caching=True)
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params = SamplingParams(max_tokens=512, temperature=0.7)
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# Continuous batching — 매 새 request 의 mid-batch 의 join.
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outputs = llm.generate(prompts, params)
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```
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### Game loop (fixed timestep)
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```rust
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const DT: f32 = 1.0 / 60.0;
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let mut acc = 0.0;
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let mut last = Instant::now();
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loop {
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let now = Instant::now();
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acc += (now - last).as_secs_f32();
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last = now;
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while acc >= DT {
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physics_step(DT);
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acc -= DT;
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}
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render(acc / DT); // interpolate
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}
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```
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### RTOS task (Zephyr)
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```c
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K_THREAD_DEFINE(ctrl_tid, 1024, control_loop, NULL, NULL, NULL,
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K_PRIO_PREEMPT(2), 0, 0);
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void control_loop(void *p1, void *p2, void *p3) {
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while (1) {
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read_sensors();
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compute_pid();
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actuate();
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k_sleep(K_MSEC(10)); // 100Hz hard deadline
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}
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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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| Safety-critical (medical, auto) | Hard RT — RTOS, formal verification |
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| LLM chat | SSE streaming + prompt cache |
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| Multiplayer game | UDP + WebRTC / custom protocol |
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| Voice/video call | WebRTC |
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| HFT | Kernel bypass (DPDK), FPGA |
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| Robotics | ROS 2 + Zephyr/PREEMPT_RT Linux |
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**기본값**: SSE + Anthropic streaming for LLM, WebSocket for bidirectional chat.
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## 🔗 Graph
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- 부모: [[Distributed-Systems]]
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- 변형: [[Streaming]]
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- 응용: [[WebRTC]] · [[Game-Loop]]
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- Adjacent: [[Latency-Optimization]] · [[LLM_Optimization_and_Deployment_Strategies|vLLM]]
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## 🤖 LLM 활용
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**언제**: 매 user-facing chat (TTFT < 500ms), 매 long-output (token streaming UX), tool-use loops.
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**언제 X**: batch processing (use Batch API — 50% cheaper), embeddings (single-shot), latency-insensitive analytics.
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## ❌ 안티패턴
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- **Block on full response**: 매 user 의 spinner 의 30s — 매 stream 의 use.
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- **Soft RT 의 hard guarantees claim**: Linux + GC 의 hard RT X.
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- **No timeout**: hung connection 의 leak — `httpx.Timeout(30.0, connect=5.0)`.
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- **No backpressure**: producer 의 consumer 의 outpace → OOM.
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- **Synchronous in event loop**: `time.sleep` 의 asyncio 의 block.
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## 🧪 검증 / 중복
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- Verified (vLLM docs, Anthropic streaming API, WebRTC RFC, Zephyr docs).
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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 — RT systems + web streaming + LLM inference unified |
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