2026년의 AI 에이전트 아키텍처: 완전한 개발자

┌─────────────────────────────────────┐ │ Perception Layer │ ← Inputs: text, API data, tool results ├─────────────────────────────────────┤ │ Planning / Reasoning Engine │ ← ReAct loop: Think → Act → Observe ├─────────────────────────────────────┤ │ Memory Subsystem │ ← Short-term (context) + Long-term (vector/DB) ├─────────────────────────────────────┤ │ Tool Execution Layer │ ← Function calls, MCP tools, APIs ├─────────────────────────────────────┤ │ Output / Action Interface │ ← Text, structured data, side effects └─────────────────────────────────────┘

// Registering an MCP tool in LangGraph (simplified) const mcpClient = new MCPClient({ serverUrl: "mcp://localhost:3001" }); const tools = await mcpClient.listTools(); const agent = createReactAgent({ llm, tools });

Do you need multi-agent support? ├── No → Single-agent: OpenAI Agents SDK (fastest) or Claude Agent SDK (best reasoning) └── Yes → Are you on a specific cloud? ├── AWS → Strands Agents ├── GCP → Google ADK └── Cloud-agnostic → Complex stateful workflows? → LangGraph Rapid team setup? → CrewAI Research / custom patterns? → AG2

// LangGraph with LangSmith tracing (simplified) const graph = new StateGraph(AgentState) .addNode("researcher", researcherAgent) .addNode("writer", writerAgent) .compile({ checkpointer }); // Set LANGCHAIN_TRACING_V2=true + LANGCHAIN_API_KEY // Every run is automatically traced in LangSmith

User Request ↓ [Orchestrator Agent] ↓ ↓ [Research Agent] [Competitor Agent] ← Run in parallel ↓ ↓ [Synthesis Agent] ← Receives both outputs ↓ [Publishing Agent] ← Writes final output to CMS via MCP tool

// state.js const AgentState = Annotation.Root({ task: Annotation({ reducer: (a, b) => b }), research_results: Annotation({ reducer: (a, b) => [...(a || []), ...b] }), synthesis: Annotation({ reducer: (a, b) => b }), final_output: Annotation({ reducer: (a, b) => b }), error: Annotation({ reducer: (a, b) => b }), iteration_count: Annotation({ reducer: (a, b) => (a || 0) + 1 }), });

// research_agent.js const researchAgent = async (state) => { if (state.iteration_count > 20) { return { error: "Max iterations exceeded", final_output: null }; } const tools = [webSearchTool, mcpScraperTool, cacheReadTool]; const result = await llm.invoke({ messages: [systemPrompt, ...state.messages], tools, max_tokens: 4096, }); return { research_results: [result.content] }; };

// tools/mcp-registry.js const mcpClient = new MCPClient({ servers: { "web-scraper": { url: "mcp://scraper-service:3001" }, "cms-publisher": { url: "mcp://cms-service:3002" }, "vector-memory": { url: "mcp://memory-service:3003" }, }, }); const tools = await mcpClient.listTools(); // Auto-discovers all tools

// graph.js const workflow = new StateGraph(AgentState) .addNode("orchestrator", orchestratorAgent) .addNode("researcher", researchAgent) .addNode("synthesizer", synthesizerAgent) .addNode("publisher", publisherAgent) .addNode("error_handler", errorHandlerAgent) .addEdge(START, "orchestrator") .addConditionalEdges("orchestrator", routeByTask, { research: "researcher", error: "error_handler", }) .addEdge("researcher", "synthesizer") .addConditionalEdges("synthesizer", checkQuality, { pass: "publisher", fail: "researcher", // Retry with feedback }) .addEdge("publisher", END) .compile({ checkpointer: new PostgresCheckpointer(dbConfig) });