2026 年の AI エージェント アーキテクチャ: 完全な開発者\

┌─────────────────────────────────────┐
│         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) });