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-cognitive-computing
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title: Cognitive Computing
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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: [cognitive computing, IBM Watson, autonomous agent, multimodal AI, contextual AI]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.83
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verification_status: applied
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tags: [cognitive-computing, agent, multimodal, contextual, llm, watson, ibm, history]
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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: AI history / current
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applicable_to: [Enterprise AI, Multimodal Agent, Watson Migration]
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---
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# Cognitive Computing
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## 매 한 줄
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> **"매 calculator 의 X — 매 cognitive partner"**. IBM Watson era 의 term 가, 매 modern: 매 LLM-based agentic system 의 redefine. 매 contextual + adaptive + multimodal + autonomous. 매 enterprise era 의 reference.
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## 매 핵심
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### 매 5 attribute (IBM 의 original)
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1. **Contextual**: 매 situation 의 understand.
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2. **Adaptive**: 매 self-learning.
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3. **Iterative + Stateful**: 매 conversation 의 maintain.
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4. **Interactive**: 매 multimodal interface.
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5. **Personalized**.
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### 매 history
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#### IBM Watson (2011)
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- **Jeopardy** champion (Brad Rutter, Ken Jennings).
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- 매 hybrid (rules + ML).
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- 매 enterprise (medical, finance) 의 push.
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- 매 결국 매 narrow ROI.
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#### IBM Watson Health (2015-2022)
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- 매 oncology / diagnosis.
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- 매 commercial failure.
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- 매 sold off (Francisco Partners 2022).
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- 매 lesson: 매 hype + 매 narrow capability gap.
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#### Deep Blue (1997)
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- 매 chess (Kasparov).
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- 매 specialized.
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- 매 cognitive computing 의 ancestor.
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### 매 modern (2022+)
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- 매 LLM 의 takeover.
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- 매 ChatGPT, Claude, Gemini 의 cognitive computing 의 새 form.
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- 매 agentic workflow.
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- 매 multimodal native.
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### 매 industry term 변화
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| Era | Term |
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|---|---|
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| 1980s | Expert System |
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| 2010s | Cognitive Computing |
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| 2018-2022 | AI / ML |
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| 2023+ | Generative AI / LLM |
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| 2024+ | Agentic AI |
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→ 매 hype cycle 의 typical.
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### 매 enterprise application
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1. **Customer service**: 매 chatbot.
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2. **Document understanding**: 매 PDF parsing.
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3. **Knowledge management**: 매 RAG.
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4. **Decision support**: 매 medical diagnosis (caution).
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5. **Process automation**: 매 RPA + LLM.
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6. **Personalization**: 매 recommendation.
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### 매 Watson → LLM migration
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- 매 Watson 의 customer 의 LLM platform 의 transition.
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- 매 case-based reasoning → 매 RAG.
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- 매 NLU services → 매 LLM API.
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## 💻 패턴 (응용 — modern equivalent)
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### Watson → LLM equivalent
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| Watson Service | LLM Equivalent |
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|---|---|
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| Watson Assistant (chatbot) | OpenAI Assistants / Claude with tools |
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| Watson Discovery | Vector DB + RAG |
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| Natural Language Understanding | LLM zero-shot |
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| Watson Tone Analyzer | Sentiment via LLM |
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| Watson Visual Recognition | GPT-4V / Claude vision / Gemini |
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| Watson Speech | Whisper / Deepgram |
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| Watson Knowledge Studio | LLM fine-tune |
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### Modern cognitive system (RAG + agent)
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```python
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from langchain.agents import create_react_agent
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from langchain.vectorstores import Chroma
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from langchain.tools import tool
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# 매 knowledge base
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kb = Chroma.from_documents(corporate_docs, embeddings)
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@tool
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def search_kb(query: str) -> str:
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"""Search internal knowledge base."""
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return kb.similarity_search(query, k=5)
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@tool
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def search_web(query: str) -> str:
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"""Search the web."""
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return search_engine(query)
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agent = create_react_agent(
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llm=ChatOpenAI(model='gpt-4o'),
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tools=[search_kb, search_web, calculator, send_email],
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prompt=cognitive_prompt,
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)
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```
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### Multimodal (vision + speech + text)
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```python
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from openai import OpenAI
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client = OpenAI()
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# 매 vision
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vision_response = client.chat.completions.create(
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model='gpt-4o',
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messages=[{
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'role': 'user',
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'content': [
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{'type': 'text', 'text': 'What is shown in this image?'},
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{'type': 'image_url', 'image_url': {'url': image_url}},
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],
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}],
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)
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# 매 audio (Whisper)
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audio_transcript = client.audio.transcriptions.create(
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model='whisper-1',
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file=audio_file,
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)
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# 매 speech synthesis
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speech = client.audio.speech.create(
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model='tts-1',
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voice='alloy',
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input='Hello world',
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)
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```
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### Adaptive (online learning)
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```python
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class AdaptiveAssistant:
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def __init__(self, base_llm):
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self.llm = base_llm
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self.user_profile = {}
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def respond(self, user_id, query):
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profile = self.user_profile.get(user_id, {})
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# 매 personalized prompt
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prompt = f"""User profile (learned over time):
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- Communication style: {profile.get('style', 'unknown')}
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- Expertise level: {profile.get('expertise', 'unknown')}
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- Preferences: {profile.get('preferences', {})}
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Query: {query}
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Adapt response to this user."""
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response = self.llm.generate(prompt)
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return response
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def learn(self, user_id, feedback):
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# 매 update profile based on feedback
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if user_id not in self.user_profile:
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self.user_profile[user_id] = {}
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update_profile(self.user_profile[user_id], feedback)
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```
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### Stateful conversation
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```python
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class CognitiveSession:
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def __init__(self, max_history=20):
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self.history = []
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self.max_history = max_history
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def respond(self, user_input):
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self.history.append({'role': 'user', 'content': user_input})
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# 매 context window management
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if len(self.history) > self.max_history:
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old = self.history[:5]
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summary = summarize(old)
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self.history = [{'role': 'system', 'content': f'Earlier: {summary}'}] + self.history[5:]
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response = llm.chat(self.history)
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self.history.append({'role': 'assistant', 'content': response})
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return response
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```
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### Enterprise integration (Watson-style replacement)
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```python
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class EnterpriseAssistant:
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def __init__(self):
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self.kb = ChromaCollection('corporate_docs')
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self.crm = SalesforceClient()
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self.tickets = JiraClient()
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self.email = OutlookClient()
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def handle(self, user, query):
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# 매 context 의 enrich
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user_context = self.crm.get_user_context(user.id)
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recent_tickets = self.tickets.recent_for(user.id)
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# 매 RAG
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relevant_docs = self.kb.search(query, k=5)
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# 매 LLM 의 process
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response = llm.generate(f"""User: {user.name}, role: {user.role}
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Recent tickets: {recent_tickets}
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Relevant docs: {relevant_docs}
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Query: {query}
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Provide a tailored response with citations.""")
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# 매 action 의 execute (if needed)
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if requires_action(response):
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execute_action(response, user)
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return response
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```
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## 🤔 결정 기준
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| 상황 | Modern Approach |
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|---|---|
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| Q&A | LLM + RAG |
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| Multi-step task | Agent (LangChain) |
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| Multimodal | GPT-4V / Claude / Gemini |
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| Voice | Whisper + LLM + TTS |
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| Specialized domain | Fine-tune (LoRA) + RAG |
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| Watson migration | OpenAI / Anthropic / Bedrock + custom |
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| Privacy-critical | Self-hosted Llama / Mistral |
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**기본값**: 매 cognitive computing 의 modern form 의 LLM agent + RAG + multimodal.
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## 🔗 Graph
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- 부모: [[AI]]
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- 변형: [[IBM-Watson]]
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- 응용: [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[RAG]] · [[Multimodal-AI]] · [[Cognitive-Architecture]]
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- Adjacent: [[Artificial-Intelligence]] · [[Biological-Intelligence]] · [[Bayesian-Brain-Hypothesis]] · [[Beliefs]]
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## 🤖 LLM 활용
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**언제**: 매 enterprise AI strategy. 매 Watson migration. 매 contextual assistant. 매 multimodal app.
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**언제 X**: 매 simple lookup (no cognition needed). 매 deterministic rule.
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## ❌ 안티패턴
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- **Cognitive computing 의 hype 의 buy**: 매 narrow capability 의 general expectation.
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- **Watson era 의 stuck**: 매 LLM 의 leverage X.
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- **No state / context**: 매 cognitive 의 X.
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- **Single-modal limit**: 매 modern 의 multimodal expect.
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- **No personalization**: 매 generic 의 only.
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## 🧪 검증 / 중복
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- Verified (IBM Watson history, modern LLM era).
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- 신뢰도 B.
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- Related: [[Cognitive-Architecture]] · [[Artificial-Intelligence]] · [[Biological-Intelligence]] · [[Asset-Specific-Knowledge]] (RAG).
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Watson history + modern equivalent + 매 RAG / multimodal / adaptive code |
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