REFACTOR(repo): simplify project structure

- Move services/backend to langgraph/
- Move deploy/docker/Dockerfile to Dockerfile
- Remove deploy/, services/ folders
- Update GitHub Actions workflow paths
- Remove kustomization update logic (managed by K3S-HOME/applications)

langgraph/agents/orchestrator.py

@@ -0,0 +1,192 @@
+"""
+Orchestrator Agent (Claude 4.5)
+전체 조율 및 최종 의사결정
+"""
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import SystemMessage, HumanMessage
+from .state import AgentState
+from tools.bash_tool import bash_tools
+import os
+
+
+# Claude 4.5 모델 초기화
+claude_orchestrator = ChatAnthropic(
+    model="claude-sonnet-4-20250514",
+    api_key=os.getenv("ANTHROPIC_API_KEY"),
+    temperature=0.7
+)
+
+
+ORCHESTRATOR_PROMPT = """You are the Orchestrator of a K8s Analysis & Decision System.
+
+## Role
+Determine request type and route to appropriate agents.
+
+## Request Types
+
+### Type 1: Information Query (정보 조회)
+Keywords: "알려줘", "조회", "확인", "보여줘", "찾아줘", "검색", "상태", "비밀번호", "목록", "리스트"
+Examples:
+- "PostgreSQL 비밀번호 알려줘"
+- "현재 Pod 상태 확인해줘"
+- "Secret 목록 보여줘"
+Workflow: research → end
+
+### Type 2: Deployment Decision (도입 결정)
+Keywords: "도입", "설치", "배포", "필요", "결정", "추천", "분석", "사용"
+Examples:
+- "Tekton 도입할까?"
+- "Harbor가 필요한지 분석해줘"
+Workflow: planning → research → prompt_generator → end
+
+## Available Agents
+- planning: Plan deployment requirements (deployment_decision only)
+- research: Analyze cluster state or retrieve information
+- decision: Make final decision (추천/비추천) (deployment_decision only)
+- prompt_generator: Generate implementation guide for other AI (deployment_decision, only if approved)
+- end: Complete the task
+
+## Decision Logic
+
+**First, determine request_type (첫 호출 시만):**
+- If user wants information → request_type = "information_query"
+- If user wants deployment decision → request_type = "deployment_decision"
+
+**Then route based on request_type:**
+
+### For information_query:
+- Current state: start → NEXT_AGENT: research
+- Current state: research done → NEXT_AGENT: end
+
+### For deployment_decision:
+- Current state: start → NEXT_AGENT: planning
+- Current state: planning done → NEXT_AGENT: research
+- Current state: research done → NEXT_AGENT: decision
+- Current state: decision done (추천) → NEXT_AGENT: prompt_generator
+- Current state: decision done (비추천) → NEXT_AGENT: end
+- Current state: prompt_generator done → NEXT_AGENT: end
+
+Check state.get("task_plan"), state.get("research_data"), state.get("decision_report"), state.get("implementation_prompt") to determine current progress.
+
+## Output Format
+REQUEST_TYPE: <information_query|deployment_decision>
+NEXT_AGENT: <agent_name>
+REASON: <brief reason>
+
+Analyze user intent carefully to choose the correct request type.
+"""
+
+
+def orchestrator_node(state: AgentState) -> AgentState:
+    """
+    Orchestrator 노드: 전체 워크플로우 조율
+    """
+    messages = state["messages"]
+    iteration_count = state.get("iteration_count", 0)
+
+    # 컨텍스트 구성
+    context_parts = [f"현재 반복 횟수: {iteration_count}/2"]
+
+    if state.get("task_plan"):
+        context_parts.append(f"✅ 계획 수립 완료")
+
+    if state.get("research_data"):
+        context_parts.append(f"✅ 클러스터 분석 완료")
+
+    if state.get("implementation_prompt"):
+        context_parts.append(f"✅ 구현 프롬프트 생성 완료")
+
+    context = "\n".join(context_parts)
+
+    # 사용자 요청
+    user_request = messages[-1]["content"] if messages else ""
+
+    # Claude에 bash 도구 바인딩
+    claude_with_tools = claude_orchestrator.bind_tools(bash_tools)
+
+    # Claude 호출
+    response = claude_with_tools.invoke([
+        SystemMessage(content=ORCHESTRATOR_PROMPT),
+        HumanMessage(content=f"사용자 요청: {user_request}\n\n현재 상태:\n{context}")
+    ])
+
+    # Tool calls 처리
+    tool_outputs = []
+    if hasattr(response, 'tool_calls') and response.tool_calls:
+        for tool_call in response.tool_calls:
+            tool_name = tool_call['name']
+            tool_args = tool_call.get('args', {})
+
+            try:
+                # tool_name에 따라 올바른 도구 선택
+                from tools.bash_tool import execute_bash, execute_host
+                if tool_name == "execute_host":
+                    tool_func = execute_host
+                else:
+                    tool_func = execute_bash
+                tool_result = tool_func.invoke(tool_args)
+                tool_outputs.append(f"\n🔧 **Orchestrator {tool_name}({tool_args.get('command', '')[:50]}...)**:\n{tool_result}")
+            except Exception as e:
+                tool_outputs.append(f"\n❌ **{tool_name}** failed: {str(e)}")
+
+        # Tool 결과와 함께 재호출
+        if tool_outputs:
+            tool_context = "\n".join(tool_outputs)
+            response = claude_orchestrator.invoke([
+                SystemMessage(content=ORCHESTRATOR_PROMPT),
+                HumanMessage(content=f"사용자 요청: {user_request}\n\n현재 상태:\n{context}"),
+                HumanMessage(content=f"도구 실행 결과:\n{tool_context}")
+            ])
+
+    content = response.content
+    if tool_outputs:
+        content = "\n".join(tool_outputs) + "\n\n" + content
+
+    # 요청 타입 파싱
+    request_type = state.get("request_type")  # 기존 값 유지
+    if "REQUEST_TYPE:" in content and not request_type:
+        for line in content.split("\n"):
+            if line.startswith("REQUEST_TYPE:"):
+                request_type = line.split(":")[1].strip()
+                state["request_type"] = request_type
+                break
+
+    # 다음 에이전트 파싱
+    next_agent = "planning"  # 기본값
+    if "NEXT_AGENT:" in content:
+        for line in content.split("\n"):
+            if line.startswith("NEXT_AGENT:"):
+                next_agent = line.split(":")[1].strip()
+                break
+
+    # request_type에 따른 라우팅 보정
+    if request_type == "information_query":
+        # 정보 조회: Planning 건너뛰기
+        if next_agent == "planning":
+            next_agent = "research"
+    elif request_type == "deployment_decision":
+        # 의사결정: 순서 보장 (planning → research → decision → prompt_generator(추천시만) → end)
+        task_plan = state.get("task_plan")
+        research_data = state.get("research_data")
+        decision_report = state.get("decision_report")
+        implementation_prompt = state.get("implementation_prompt")
+
+        if not task_plan:
+            next_agent = "planning"
+        elif not research_data:
+            next_agent = "research"
+        elif not decision_report:
+            next_agent = "decision"
+        elif decision_report and decision_report.get("recommendation") == "approve" and not implementation_prompt:
+            next_agent = "prompt_generator"
+        else:
+            next_agent = "end"
+
+    # 메시지 추가
+    state["messages"].append({
+        "role": "orchestrator",
+        "content": content
+    })
+    state["current_agent"] = next_agent
+
+    return state