AIエージェントワークフローエンジンをゼロから構築：Javaで本番級オーケストレーションシステムを構築
なぜ自前のAIエージェントワークフローエンジンが必要か？
2026年、AIエージェントは「おもちゃ」から「本番」へ移行したが、既存フレームワークの限界が顕著になっている：
| フレームワーク | 言語 | ワークフローモード | DAG対応 | 状態永続化 | 本番対応 |
|---|---|---|---|---|---|
| LangChain/LangGraph | Python | ステートマシン | ✅ | ❌（自前必要） | ⚠️ |
| CrewAI | Python | 順序/階層 | ❌ | ❌ | ❌ |
| AutoGen | Python | 会話型 | ❌ | ❌ | ❌ |
| Spring AI | Java | 線形チェーン | ❌ | ❌ | ⚠️ |
| Dify | Python/Go | ビジュアルDAG | ✅ | ✅ | ✅ |
核心のペインポイント：
- LangChainエコシステムがPythonにロックイン — Javaエンタープライズプロジェクトで直接再利用不可、跨言語呼び出しでシリアライズオーバーヘッドとデバッグ地獄
- Spring AIは線形チェーンのみ対応 —
ChatClient → Tool → Advisorのチェーン呼び出しでは複雑な分岐や並列ロジックを表現できない - 状態管理が不在 — エージェント実行中のクラッシュからチェックポイントで再開できない
- Multi-Agent協調が原始的 — Sequential/Parallel/Router/Debate等の協調パターンが組み込みでない
実例：ある金融会社がLangGraphでリスク管理エージェントを構築、Python GIL制限で並発ボトルネック発生、単機QPSわずか120；Java自前エンジンに移行後QPS 1800に向上。
エージェントワークフローエンジンのアーキテクチャ設計
5層アーキテクチャ概要
┌─────────────────────────────────────────────────────────────────┐
│ API層（Layer 1） │
│ REST Controller │ GraphQL │ WebSocket（SSEストリーミング） │
├─────────────────────────────────────────────────────────────────┤
│ オーケストレーション層（Layer 2） │
│ WorkflowEngine │ DAGScheduler │ ConditionRouter │ LoopHandler │
├─────────────────────────────────────────────────────────────────┤
│ ノード層（Layer 3） │
│ LLMNode │ ToolNode │ TransformNode │ SubWorkflowNode │
│ HumanApprovalNode │ ParallelGroupNode │ RouterNode │
├─────────────────────────────────────────────────────────────────┤
│ 状態管理層（Layer 4） │
│ StateMachine（Spring Statemachine）│ CheckpointManager │
│ Redis永続化 │ 実行コンテキスト（WorkflowContext） │
├─────────────────────────────────────────────────────────────────┤
│ インフラ層（Layer 5） │
│ ToolRegistry │ ModelRegistry │ EventBus │ AuditLogger │
│ MetricsCollector │ RetryPolicy │ CircuitBreaker │
└─────────────────────────────────────────────────────────────────┘
コアドメインモデル
public class WorkflowDefinition {
private String workflowId;
private String name;
private String version;
private List<NodeDefinition> nodes;
private List<EdgeDefinition> edges;
private Map<String, Object> metadata;
private WorkflowConfig config;
}
public class NodeDefinition {
private String nodeId;
private String type;
private Map<String, Object> properties;
private RetryPolicy retryPolicy;
private TimeoutConfig timeout;
}
public class EdgeDefinition {
private String sourceId;
private String targetId;
private String condition;
private EdgeType type;
}
public enum EdgeType {
NORMAL, CONDITIONAL, PARALLEL, LOOP
}
ワークフロー実行コンテキスト
public class WorkflowContext {
private String executionId;
private String workflowId;
private Map<String, Object> variables;
private Map<String, NodeResult> nodeResults;
private WorkflowState currentState;
private Instant startTime;
private String checkpointKey;
public <T> T getVariable(String key, Class<T> type) {
Object value = variables.get(key);
return type.cast(value);
}
public void setVariable(String key, Object value) {
variables.put(key, value);
}
public NodeResult getNodeResult(String nodeId) {
return nodeResults.get(nodeId);
}
}
DAGタスクスケジューラ：コア実装
DAG（有向非巡回グラフ）はワークフローエンジンのスケジューリングコアである。3つの重要な問題を解決する必要がある：循環検出、トポロジカルソート、依存関係のないノードの並列実行。
循環検出アルゴリズム
@Component
public class CycleDetector {
public boolean hasCycle(List<NodeDefinition> nodes, List<EdgeDefinition> edges) {
Map<String, Set<String>> adjacency = buildAdjacencyMap(nodes, edges);
Set<String> visited = new HashSet<>();
Set<String> recursionStack = new HashSet<>();
for (NodeDefinition node : nodes) {
if (dfsCycleCheck(node.getNodeId(), adjacency, visited, recursionStack)) {
return true;
}
}
return false;
}
private boolean dfsCycleCheck(String nodeId,
Map<String, Set<String>> adjacency,
Set<String> visited,
Set<String> recursionStack) {
visited.add(nodeId);
recursionStack.add(nodeId);
for (String neighbor : adjacency.getOrDefault(nodeId, Set.of())) {
if (!visited.contains(neighbor)) {
if (dfsCycleCheck(neighbor, adjacency, visited, recursionStack)) {
return true;
}
} else if (recursionStack.contains(neighbor)) {
return true;
}
}
recursionStack.remove(nodeId);
return false;
}
private Map<String, Set<String>> buildAdjacencyMap(
List<NodeDefinition> nodes, List<EdgeDefinition> edges) {
Map<String, Set<String>> adjacency = new HashMap<>();
for (NodeDefinition node : nodes) {
adjacency.put(node.getNodeId(), new HashSet<>());
}
for (EdgeDefinition edge : edges) {
adjacency.get(edge.getSourceId()).add(edge.getTargetId());
}
return adjacency;
}
}
トポロジカルソートとレベル分割
@Component
public class TopologicalSorter {
public List<Set<String>> sortIntoLevels(List<NodeDefinition> nodes,
List<EdgeDefinition> edges) {
Map<String, Set<String>> inEdges = new HashMap<>();
Map<String, Set<String>> outEdges = new HashMap<>();
for (NodeDefinition node : nodes) {
inEdges.put(node.getNodeId(), new HashSet<>());
outEdges.put(node.getNodeId(), new HashSet<>());
}
for (EdgeDefinition edge : edges) {
outEdges.get(edge.getSourceId()).add(edge.getTargetId());
inEdges.get(edge.getTargetId()).add(edge.getSourceId());
}
List<Set<String>> levels = new ArrayList<>();
Set<String> processed = new HashSet<>();
while (processed.size() < nodes.size()) {
Set<String> currentLevel = new HashSet<>();
for (NodeDefinition node : nodes) {
String nodeId = node.getNodeId();
if (!processed.contains(nodeId) &&
inEdges.get(nodeId).stream().allMatch(processed::contains)) {
currentLevel.add(nodeId);
}
}
if (currentLevel.isEmpty()) {
throw new WorkflowException("DAG contains cycle, cannot sort");
}
levels.add(currentLevel);
processed.addAll(currentLevel);
}
return levels;
}
}
DAGスケジューラ：依存関係のないノードの並列実行
@Service
public class DAGScheduler {
private final TopologicalSorter sorter;
private final CycleDetector cycleDetector;
private final NodeExecutorFactory executorFactory;
private final CheckpointManager checkpointManager;
@Async("workflowExecutor")
public CompletableFuture<WorkflowResult> execute(
WorkflowDefinition workflow, WorkflowContext context) {
if (cycleDetector.hasCycle(workflow.getNodes(), workflow.getEdges())) {
throw new WorkflowException("Workflow contains cycle");
}
List<Set<String>> levels = sorter.sortIntoLevels(
workflow.getNodes(), workflow.getEdges());
for (int i = 0; i < levels.size(); i++) {
Set<String> levelNodes = levels.get(i);
log.info("Executing level {} with {} nodes: {}", i, levelNodes.size(), levelNodes);
List<CompletableFuture<NodeResult>> futures = levelNodes.stream()
.map(nodeId -> executeNode(workflow, nodeId, context))
.toList();
CompletableFuture<Void> levelFuture = CompletableFuture.allOf(
futures.toArray(new CompletableFuture[0]));
levelFuture.join();
for (CompletableFuture<NodeResult> future : futures) {
NodeResult result = future.join();
context.getNodeResults().put(result.getNodeId(), result);
}
checkpointManager.saveCheckpoint(context);
}
return CompletableFuture.completedFuture(
WorkflowResult.success(context));
}
private CompletableFuture<NodeResult> executeNode(
WorkflowDefinition workflow, String nodeId, WorkflowContext context) {
NodeDefinition nodeDef = workflow.getNodes().stream()
.filter(n -> n.getNodeId().equals(nodeId))
.findFirst()
.orElseThrow();
NodeExecutor executor = executorFactory.getExecutor(nodeDef.getType());
return executor.execute(nodeDef, context);
}
}
スケジューリング可視化：
Level 0: [意図分類] ← 依存なし、即時実行
Level 1: [注文照会] [ナレッジ検索] ← Level 0に依存、並列実行
Level 2: [結果集約] ← Level 1の全完了に依存
Level 3: [応答生成] ← Level 2に依存
ツール登録と呼び出しフレームワーク
ToolRegistry：統合ツール登録センター
@Service
public class ToolRegistry {
private final Map<String, ToolDescriptor> tools = new ConcurrentHashMap<>();
private final Map<String, ToolExecutor> executors = new ConcurrentHashMap<>();
public void register(ToolDescriptor descriptor, ToolExecutor executor) {
tools.put(descriptor.getName(), descriptor);
executors.put(descriptor.getName(), executor);
log.info("Registered tool: {} with schema: {}",
descriptor.getName(), descriptor.getInputSchema());
}
public ToolExecutionResult execute(String toolName, Map<String, Object> input) {
ToolExecutor executor = executors.get(toolName);
if (executor == null) {
throw new ToolNotFoundException("Tool not found: " + toolName);
}
ToolDescriptor descriptor = tools.get(toolName);
validateInput(descriptor, input);
return executor.execute(input);
}
public List<ToolDescriptor> getAllTools() {
return new ArrayList<>(tools.values());
}
public List<Map<String, Object>> getOpenAIToolSchemas() {
return tools.values().stream()
.map(this::convertToOpenAISchema)
.toList();
}
private void validateInput(ToolDescriptor descriptor, Map<String, Object> input) {
JsonSchema schema = JsonSchemaFactory.getInstance()
.getSchema(descriptor.getInputSchema());
Set<ValidationMessage> errors = schema.validate(input);
if (!errors.isEmpty()) {
throw new ToolValidationException(
"Input validation failed: " + errors);
}
}
}
ToolExecutorインターフェースと実装
public interface ToolExecutor {
ToolExecutionResult execute(Map<String, Object> input);
String getName();
String getDescription();
}
@Component
public class HttpToolExecutor implements ToolExecutor {
private final RestTemplate restTemplate;
@Override
public ToolExecutionResult execute(Map<String, Object> input) {
String url = (String) input.get("url");
String method = (String) input.getOrDefault("method", "GET");
Map<String, String> headers = (Map<String, String>) input.getOrDefault("headers", Map.of());
Object body = input.get("body");
HttpHeaders httpHeaders = new HttpHeaders();
headers.forEach(httpHeaders::set);
HttpEntity<Object> entity = new HttpEntity<>(body, httpHeaders);
ResponseEntity<String> response = restTemplate.exchange(
url, HttpMethod.valueOf(method), entity, String.class);
return ToolExecutionResult.builder()
.success(true)
.data(response.getBody())
.metadata(Map.of("statusCode", response.getStatusCode().value()))
.build();
}
@Override
public String getName() { return "http_request"; }
@Override
public String getDescription() { return "Execute HTTP request to external API"; }
}
@Component
public class DatabaseToolExecutor implements ToolExecutor {
private final JdbcTemplate jdbcTemplate;
@Override
public ToolExecutionResult execute(Map<String, Object> input) {
String sql = (String) input.get("sql");
List<Object> params = (List<Object>) input.getOrDefault("params", List.of());
if (!isSafeQuery(sql)) {
throw new SecurityException("Unsafe SQL query blocked: " + sql);
}
List<Map<String, Object>> results = jdbcTemplate.queryForList(sql, params.toArray());
return ToolExecutionResult.builder()
.success(true)
.data(results)
.build();
}
private boolean isSafeQuery(String sql) {
String upper = sql.toUpperCase().trim();
return upper.startsWith("SELECT") && !upper.contains("DROP")
&& !upper.contains("DELETE") && !upper.contains("UPDATE");
}
@Override
public String getName() { return "database_query"; }
@Override
public String getDescription() { return "Execute read-only SQL query"; }
}
@Component
public class FileToolExecutor implements ToolExecutor {
@Value("${tool.file.base-dir}")
private String baseDir;
@Override
public ToolExecutionResult execute(Map<String, Object> input) {
String operation = (String) input.get("operation");
String path = (String) input.get("path");
Path resolvedPath = Paths.get(baseDir, path).normalize();
if (!resolvedPath.startsWith(Paths.get(baseDir))) {
throw new SecurityException("Path traversal detected");
}
return switch (operation) {
case "read" -> readFile(resolvedPath);
case "list" -> listDirectory(resolvedPath);
case "write" -> writeFile(resolvedPath, (String) input.get("content"));
default -> throw new IllegalArgumentException("Unknown operation: " + operation);
};
}
@Override
public String getName() { return "file_operation"; }
@Override
public String getDescription() { return "Read, list, or write files within allowed directory"; }
}
ツール登録設定
@Configuration
public class ToolRegistryConfig {
@Bean
public ToolRegistry toolRegistry(
HttpToolExecutor httpTool,
DatabaseToolExecutor dbTool,
FileToolExecutor fileTool) {
ToolRegistry registry = new ToolRegistry();
registry.register(ToolDescriptor.builder()
.name("http_request")
.description("Execute HTTP request to external API")
.inputSchema("""
{
"type": "object",
"properties": {
"url": {"type": "string", "format": "uri"},
"method": {"type": "string", "enum": ["GET","POST","PUT","DELETE"]},
"headers": {"type": "object"},
"body": {"type": "object"}
},
"required": ["url"]
}
""")
.build(), httpTool);
registry.register(ToolDescriptor.builder()
.name("database_query")
.description("Execute read-only SQL query")
.inputSchema("""
{
"type": "object",
"properties": {
"sql": {"type": "string"},
"params": {"type": "array"}
},
"required": ["sql"]
}
""")
.build(), dbTool);
registry.register(ToolDescriptor.builder()
.name("file_operation")
.description("File operations within allowed directory")
.inputSchema("""
{
"type": "object",
"properties": {
"operation": {"type": "string", "enum": ["read","list","write"]},
"path": {"type": "string"},
"content": {"type": "string"}
},
"required": ["operation", "path"]
}
""")
.build(), fileTool);
return registry;
}
}
ステートマシン：エージェント実行状態管理
状態定義と遷移
┌──────────┐
作成 │ CREATED │
────────► └──────────┘
│ start
▼
┌──────────┐
│ RUNNING │◄────────┐
└──────────┘ │
│ │ resume │
pause │ │ complete │
▼ ▼ │
┌──────────┐ ┌──────────┐ │
│ PAUSED │ │COMPLETED │ │
└──────────┘ └──────────┘ │
│ │
│ resume │
└──────────────────────┘
│ error
▼
┌──────────┐
│ FAILED │
└──────────┘
Spring Statemachine設定
@Configuration
@EnableStateMachineFactory
public class WorkflowStateMachineConfig
extends EnumStateMachineConfigurerAdapter<WorkflowState, WorkflowEvent> {
@Override
public void configure(StateMachineStateConfigurer<WorkflowState, WorkflowEvent> states)
throws Exception {
states
.withStates()
.initial(WorkflowState.CREATED)
.states(EnumSet.allOf(WorkflowState.class))
.end(WorkflowState.COMPLETED)
.end(WorkflowState.FAILED);
}
@Override
public void configure(StateMachineTransitionConfigurer<WorkflowState, WorkflowEvent> transitions)
throws Exception {
transitions
.withExternal()
.source(WorkflowState.CREATED).target(WorkflowState.RUNNING)
.event(WorkflowEvent.START)
.and()
.withExternal()
.source(WorkflowState.RUNNING).target(WorkflowState.PAUSED)
.event(WorkflowEvent.PAUSE)
.and()
.withExternal()
.source(WorkflowState.PAUSED).target(WorkflowState.RUNNING)
.event(WorkflowEvent.RESUME)
.and()
.withExternal()
.source(WorkflowState.RUNNING).target(WorkflowState.COMPLETED)
.event(WorkflowEvent.COMPLETE)
.and()
.withExternal()
.source(WorkflowState.RUNNING).target(WorkflowState.FAILED)
.event(WorkflowEvent.ERROR)
.and()
.withExternal()
.source(WorkflowState.PAUSED).target(WorkflowState.FAILED)
.event(WorkflowEvent.ERROR);
}
}
public enum WorkflowState {
CREATED, RUNNING, PAUSED, COMPLETED, FAILED
}
public enum WorkflowEvent {
START, PAUSE, RESUME, COMPLETE, ERROR
}
Redisチェックポイント永続化
@Component
public class RedisCheckpointManager implements CheckpointManager {
private final RedisTemplate<String, String> redisTemplate;
private final ObjectMapper objectMapper;
private static final String KEY_PREFIX = "workflow:checkpoint:";
@Override
public void saveCheckpoint(WorkflowContext context) {
String key = KEY_PREFIX + context.getExecutionId();
try {
String serialized = objectMapper.writeValueAsString(context);
redisTemplate.opsForValue().set(key, serialized, Duration.ofHours(24));
log.info("Checkpoint saved for execution: {}", context.getExecutionId());
} catch (JsonProcessingException e) {
throw new WorkflowException("Failed to save checkpoint", e);
}
}
@Override
public Optional<WorkflowContext> loadCheckpoint(String executionId) {
String key = KEY_PREFIX + executionId;
String serialized = redisTemplate.opsForValue().get(key);
if (serialized == null) {
return Optional.empty();
}
try {
return Optional.of(objectMapper.readValue(serialized, WorkflowContext.class));
} catch (JsonProcessingException e) {
throw new WorkflowException("Failed to load checkpoint", e);
}
}
@Override
public void deleteCheckpoint(String executionId) {
redisTemplate.delete(KEY_PREFIX + executionId);
}
}
チェックポイントからの実行再開
@Service
public class WorkflowRecoveryService {
private final RedisCheckpointManager checkpointManager;
private final DAGScheduler dagScheduler;
private final WorkflowDefinitionRegistry definitionRegistry;
public WorkflowResult recoverFromCheckpoint(String executionId) {
WorkflowContext context = checkpointManager.loadCheckpoint(executionId)
.orElseThrow(() -> new WorkflowException(
"No checkpoint found for: " + executionId));
WorkflowDefinition workflow = definitionRegistry.get(context.getWorkflowId());
log.info("Recovering workflow {} from checkpoint, current state: {}",
context.getWorkflowId(), context.getCurrentState());
Set<String> completedNodes = context.getNodeResults().keySet();
WorkflowDefinition remainingWorkflow = filterRemainingNodes(workflow, completedNodes);
return dagScheduler.execute(remainingWorkflow, context).join();
}
private WorkflowDefinition filterRemainingNodes(
WorkflowDefinition workflow, Set<String> completedNodes) {
List<NodeDefinition> remainingNodes = workflow.getNodes().stream()
.filter(n -> !completedNodes.contains(n.getNodeId()))
.toList();
List<EdgeDefinition> remainingEdges = workflow.getEdges().stream()
.filter(e -> !completedNodes.contains(e.getSourceId()))
.filter(e -> !completedNodes.contains(e.getTargetId()))
.toList();
return WorkflowDefinition.builder()
.workflowId(workflow.getWorkflowId())
.nodes(remainingNodes)
.edges(remainingEdges)
.build();
}
}
Multi-Agent協調パターン
4つの協調パターン比較
| パターン | ユースケース | 並発度 | レイテンシ | 一貫性 | 典型例 |
|---|---|---|---|---|---|
| Sequential | 厳密な依存関係 | 1 | 高 | 強 | 承認フロー、パイプライン |
| Parallel | 依存なし並列可能 | N | 低 | 結果整合 | マルチソースデータ集約 |
| Router | 条件分岐ルーティング | 1 | 低 | 強 | 意図ディスパッチ、ドメインルーティング |
| Debate | 多視点検証が必要 | N | 高 | 投票/重み付け | コードレビュー、意思決定 |
協調モードインターフェース
public interface AgentCollaborationMode {
WorkflowResult execute(List<AgentDefinition> agents, WorkflowContext context);
String getModeName();
}
Sequentialパターン
@Component
public class SequentialMode implements AgentCollaborationMode {
private final AgentExecutor agentExecutor;
@Override
public WorkflowResult execute(List<AgentDefinition> agents, WorkflowContext context) {
Object currentOutput = context.getVariable("input", Object.class);
for (AgentDefinition agent : agents) {
log.info("Sequential execution: agent={}, input type={}",
agent.getName(), currentOutput.getClass().getSimpleName());
AgentResult result = agentExecutor.execute(agent, currentOutput, context);
currentOutput = result.getOutput();
context.setVariable("agent_" + agent.getName() + "_output", currentOutput);
}
context.setVariable("output", currentOutput);
return WorkflowResult.success(context);
}
@Override
public String getModeName() { return "sequential"; }
}
Parallelパターン
@Component
public class ParallelMode implements AgentCollaborationMode {
private final AgentExecutor agentExecutor;
@Override
public WorkflowResult execute(List<AgentDefinition> agents, WorkflowContext context) {
Object input = context.getVariable("input", Object.class);
List<CompletableFuture<AgentResult>> futures = agents.stream()
.map(agent -> CompletableFuture.supplyAsync(
() -> agentExecutor.execute(agent, input, context)))
.toList();
CompletableFuture.allOf(futures.toArray(new CompletableFuture[0])).join();
Map<String, Object> mergedOutput = new LinkedHashMap<>();
for (int i = 0; i < agents.size(); i++) {
AgentResult result = futures.get(i).join();
mergedOutput.put(agents.get(i).getName(), result.getOutput());
}
context.setVariable("output", mergedOutput);
return WorkflowResult.success(context);
}
@Override
public String getModeName() { return "parallel"; }
}
Routerパターン
@Component
public class RouterMode implements AgentCollaborationMode {
private final AgentExecutor agentExecutor;
private final ChatClient chatClient;
@Override
public WorkflowResult execute(List<AgentDefinition> agents, WorkflowContext context) {
Object input = context.getVariable("input", Object.class);
String agentNames = agents.stream()
.map(AgentDefinition::getName)
.collect(Collectors.joining(", "));
String routingPrompt = """
Based on the input, select the most appropriate agent from: %s
Input: %s
Return ONLY the agent name, nothing else.
""".formatted(agentNames, input);
String selectedAgent = chatClient.prompt()
.user(routingPrompt)
.call()
.content();
AgentDefinition agent = agents.stream()
.filter(a -> a.getName().equals(selectedAgent.trim()))
.findFirst()
.orElse(agents.get(0));
log.info("Router selected agent: {}", agent.getName());
AgentResult result = agentExecutor.execute(agent, input, context);
context.setVariable("output", result.getOutput());
return WorkflowResult.success(context);
}
@Override
public String getModeName() { return "router"; }
}
Debateパターン
@Component
public class DebateMode implements AgentCollaborationMode {
private final AgentExecutor agentExecutor;
@Value("${workflow.debate.rounds:3}")
private int debateRounds;
@Override
public WorkflowResult execute(List<AgentDefinition> agents, WorkflowContext context) {
Object topic = context.getVariable("input", Object.class);
List<DebateRound> rounds = new ArrayList<>();
for (int round = 0; round < debateRounds; round++) {
log.info("Debate round {}/{}", round + 1, debateRounds);
List<AgentResult> roundResults = agents.stream()
.map(agent -> agentExecutor.execute(agent, topic, context))
.toList();
DebateRound debateRound = new DebateRound(round, roundResults);
rounds.add(debateRound);
topic = buildDebateContext(rounds);
}
Object finalDecision = voteOnResults(rounds);
context.setVariable("debate_rounds", rounds);
context.setVariable("output", finalDecision);
return WorkflowResult.success(context);
}
private Object voteOnResults(List<DebateRound> rounds) {
Map<String, Integer> voteCount = new HashMap<>();
for (DebateRound round : rounds) {
for (AgentResult result : round.results()) {
String conclusion = result.getOutput().toString();
voteCount.merge(conclusion, 1, Integer::sum);
}
}
return voteCount.entrySet().stream()
.max(Map.Entry.comparingByValue())
.map(Map.Entry::getKey)
.orElse("No consensus reached");
}
@Override
public String getModeName() { return "debate"; }
}
Spring AIとの深い統合
ワークフローDSL定義
@Component
public class CustomerServiceWorkflow {
private final WorkflowEngine workflowEngine;
private final ToolRegistry toolRegistry;
public WorkflowDefinition build() {
return workflowEngine.workflow("customer-service", "1.0")
.node("classify_intent")
.type("llm")
.property("prompt", "ユーザー意図を分類：注文照会/返金/苦情/オペレーター転送")
.property("model", "gpt-4o")
.node("query_order")
.type("tool")
.property("toolName", "database_query")
.property("sql", "SELECT * FROM orders WHERE order_id = :orderId")
.node("query_knowledge")
.type("tool")
.property("toolName", "http_request")
.property("url", "http://knowledge-service/api/search")
.node("aggregate")
.type("transform")
.property("expression", "merge(orderData, knowledgeData)")
.node("generate_response")
.type("llm")
.property("prompt", "以下の情報に基づいてカスタマーサービス返信を生成：{{aggregatedData}}")
.edge("classify_intent", "query_order")
.condition("intent == 'query_order'")
.edge("classify_intent", "query_knowledge")
.condition("intent == 'knowledge_query'")
.edge("query_order", "aggregate")
.edge("query_knowledge", "aggregate")
.edge("aggregate", "generate_response")
.build();
}
}
Spring AI ChatClient統合ノード
@Component
public class LLMNodeExecutor implements NodeExecutor {
private final ChatClient chatClient;
private final ToolRegistry toolRegistry;
@Override
public CompletableFuture<NodeResult> execute(
NodeDefinition node, WorkflowContext context) {
String promptTemplate = (String) node.getProperties().get("prompt");
String model = (String) node.getProperties().getOrDefault("model", "gpt-4o");
boolean useTools = (boolean) node.getProperties().getOrDefault("useTools", false);
String resolvedPrompt = resolveVariables(promptTemplate, context);
ChatClient.ChatClientRequestSpec request = chatClient.prompt()
.user(resolvedPrompt)
.options(ChatOptionsBuilder.builder()
.withModel(model)
.withTemperature(0.1)
.build());
if (useTools) {
List<Map<String, Object>> toolSchemas = toolRegistry.getOpenAIToolSchemas();
request.tools(toolSchemas);
}
String response = request.call().content();
NodeResult result = NodeResult.builder()
.nodeId(node.getNodeId())
.success(true)
.output(response)
.timestamp(Instant.now())
.build();
return CompletableFuture.completedFuture(result);
}
private String resolveVariables(String template, WorkflowContext context) {
Pattern pattern = Pattern.compile("\\{\\{(\\w+)}}");
Matcher matcher = pattern.matcher(template);
StringBuffer sb = new StringBuffer();
while (matcher.find()) {
Object value = context.getVariable(matcher.group(1), Object.class);
matcher.appendReplacement(sb, value != null ? value.toString() : "");
}
matcher.appendTail(sb);
return sb.toString();
}
@Override
public String getSupportedType() { return "llm"; }
}
REST ControllerでワークフローAPIを公開
@RestController
@RequestMapping("/api/v1/workflows")
public class WorkflowController {
private final WorkflowEngine workflowEngine;
private final WorkflowRecoveryService recoveryService;
@PostMapping("/{workflowId}/execute")
public ResponseEntity<WorkflowExecutionResponse> executeWorkflow(
@PathVariable String workflowId,
@RequestBody WorkflowExecutionRequest request) {
WorkflowContext context = WorkflowContext.builder()
.workflowId(workflowId)
.executionId(UUID.randomUUID().toString())
.variables(request.getInputs())
.build();
CompletableFuture<WorkflowResult> future = workflowEngine.execute(workflowId, context);
return ResponseEntity.accepted()
.body(WorkflowExecutionResponse.builder()
.executionId(context.getExecutionId())
.status(WorkflowState.RUNNING)
.build());
}
@GetMapping("/{workflowId}/executions/{executionId}")
public ResponseEntity<WorkflowExecutionResponse> getExecutionStatus(
@PathVariable String workflowId,
@PathVariable String executionId) {
Optional<WorkflowContext> checkpoint = recoveryService.getCheckpoint(executionId);
return checkpoint.map(ctx -> ResponseEntity.ok(
WorkflowExecutionResponse.builder()
.executionId(executionId)
.status(ctx.getCurrentState())
.nodeResults(ctx.getNodeResults())
.build()))
.orElse(ResponseEntity.notFound().build());
}
@PostMapping("/{workflowId}/executions/{executionId}/resume")
public ResponseEntity<WorkflowExecutionResponse> resumeWorkflow(
@PathVariable String workflowId,
@PathVariable String executionId) {
WorkflowResult result = recoveryService.recoverFromCheckpoint(executionId);
return ResponseEntity.ok(WorkflowExecutionResponse.builder()
.executionId(executionId)
.status(WorkflowState.COMPLETED)
.build());
}
}
ワークフローの可視化とデバッグ
実行トレースモデル
public class ExecutionTrace {
private String executionId;
private List<NodeTrace> nodeTraces;
private Instant startTime;
private Instant endTime;
private long totalDurationMs;
private WorkflowState finalState;
}
public class NodeTrace {
private String nodeId;
private String nodeName;
private Instant startTime;
private Instant endTime;
private long durationMs;
private NodeStatus status;
private String errorMessage;
private Map<String, Object> inputSnapshot;
private Map<String, Object> outputSnapshot;
private List<ToolCallTrace> toolCalls;
}
public class ToolCallTrace {
private String toolName;
private Instant callTime;
private long durationMs;
private Map<String, Object> arguments;
private Object result;
}
監査ログ記録
@Aspect
@Component
public class WorkflowAuditAspect {
private final AuditLogRepository auditLogRepository;
@Around("execution(* com.toolsku.workflow..*Executor.execute(..))")
public Object auditNodeExecution(ProceedingJoinPoint joinPoint) throws Throwable {
NodeDefinition node = (NodeDefinition) joinPoint.getArgs()[0];
WorkflowContext context = (WorkflowContext) joinPoint.getArgs()[1];
AuditLog auditLog = AuditLog.builder()
.executionId(context.getExecutionId())
.nodeId(node.getNodeId())
.nodeType(node.getType())
.startTime(Instant.now())
.build();
try {
Object result = joinPoint.proceed();
auditLog.setStatus("SUCCESS");
auditLog.setEndTime(Instant.now());
return result;
} catch (Exception e) {
auditLog.setStatus("FAILED");
auditLog.setErrorMessage(e.getMessage());
auditLog.setEndTime(Instant.now());
throw e;
} finally {
auditLogRepository.save(auditLog);
}
}
}
可視化データ生成
@Service
public class WorkflowVisualizationService {
public WorkflowGraphData generateGraphData(WorkflowDefinition workflow, ExecutionTrace trace) {
List<GraphNode> nodes = workflow.getNodes().stream()
.map(nodeDef -> {
NodeTrace nodeTrace = trace != null ?
trace.getNodeTraces().stream()
.filter(t -> t.getNodeId().equals(nodeDef.getNodeId()))
.findFirst().orElse(null) : null;
return GraphNode.builder()
.id(nodeDef.getNodeId())
.label(nodeDef.getNodeId())
.type(nodeDef.getType())
.status(nodeTrace != null ? nodeTrace.getStatus() : NodeStatus.PENDING)
.durationMs(nodeTrace != null ? nodeTrace.getDurationMs() : 0)
.build();
})
.toList();
List<GraphEdge> edges = workflow.getEdges().stream()
.map(edgeDef -> GraphEdge.builder()
.source(edgeDef.getSourceId())
.target(edgeDef.getTargetId())
.condition(edgeDef.getCondition())
.type(edgeDef.getType())
.build())
.toList();
return WorkflowGraphData.builder()
.nodes(nodes)
.edges(edges)
.build();
}
}
フォールトトレランスとリトライメカニズム
RetryPolicy定義
public class RetryPolicy {
private int maxAttempts = 3;
private Duration initialDelay = Duration.ofMillis(500);
private double multiplier = 2.0;
private Duration maxDelay = Duration.ofSeconds(30);
private Set<Class<? extends Throwable>> retryableExceptions = Set.of(
ToolExecutionException.class,
ModelTimeoutException.class,
RateLimitExceededException.class
);
private boolean exponentialBackoff = true;
}
RetryableNodeExecutor
@Component
public class RetryableNodeExecutor implements NodeExecutor {
private final Map<String, NodeExecutor> executorMap;
private final MetricsCollector metricsCollector;
@Override
public CompletableFuture<NodeResult> execute(
NodeDefinition node, WorkflowContext context) {
NodeExecutor delegate = executorMap.get(node.getType());
RetryPolicy policy = node.getRetryPolicy() != null ?
node.getRetryPolicy() : RetryPolicy.defaultPolicy();
return CompletableFuture.supplyAsync(() -> {
int attempt = 0;
Throwable lastException = null;
while (attempt < policy.getMaxAttempts()) {
attempt++;
try {
NodeResult result = delegate.execute(node, context).join();
metricsCollector.recordRetrySuccess(node.getNodeId(), attempt);
return result;
} catch (Throwable e) {
lastException = e;
if (!isRetryable(e, policy)) {
throw new WorkflowExecutionException(
"Non-retryable exception", e);
}
if (attempt < policy.getMaxAttempts()) {
Duration delay = calculateDelay(attempt, policy);
log.warn("Node {} failed (attempt {}/{}), retrying in {}ms: {}",
node.getNodeId(), attempt, policy.getMaxAttempts(),
delay.toMillis(), e.getMessage());
sleep(delay);
}
}
}
metricsCollector.recordRetryExhausted(node.getNodeId(), attempt);
throw new WorkflowExecutionException(
"Node " + node.getNodeId() + " failed after " + attempt + " attempts",
lastException);
});
}
private Duration calculateDelay(int attempt, RetryPolicy policy) {
if (!policy.isExponentialBackoff()) {
return policy.getInitialDelay();
}
long delayMs = (long) (policy.getInitialDelay().toMillis()
* Math.pow(policy.getMultiplier(), attempt - 1));
delayMs = Math.min(delayMs, policy.getMaxDelay().toMillis());
return Duration.ofMillis(delayMs);
}
private boolean isRetryable(Throwable e, RetryPolicy policy) {
return policy.getRetryableExceptions().stream()
.anyMatch(exType -> exType.isInstance(e));
}
@Override
public String getSupportedType() { return "retryable_wrapper"; }
}
CircuitBreaker統合
@Configuration
public class CircuitBreakerConfig {
@Bean
public CircuitBreakerRegistry circuitBreakerRegistry() {
CircuitBreakerConfig config = CircuitBreakerConfig.custom()
.failureRateThreshold(50)
.slowCallRateThreshold(80)
.waitDurationInOpenState(Duration.ofSeconds(30))
.slowCallDurationThreshold(Duration.ofSeconds(5))
.permittedNumberOfCallsInHalfOpenState(3)
.slidingWindowSize(10)
.slidingWindowType(CircuitBreakerConfig.SlidingWindowType.COUNT_BASED)
.build();
return CircuitBreakerRegistry.of(config);
}
@Bean
public CircuitBreaker llmCircuitBreaker(CircuitBreakerRegistry registry) {
return registry.circuitBreaker("llm-calls");
}
}
本番デプロイとパフォーマンス最適化
モニタリングメトリクス一覧
| カテゴリ | メトリクス名 | タイプ | 説明 |
|---|---|---|---|
| 実行 | workflow_execution_total | Counter | ワークフロー実行総数 |
| 実行 | workflow_execution_duration | Histogram | 実行時間分布 |
| 実行 | workflow_execution_active | Gauge | 現在アクティブな実行数 |
| ノード | node_execution_total | Counter | ノード実行総数 |
| ノード | node_execution_duration | Histogram | ノード実行時間分布 |
| ノード | node_execution_errors | Counter | ノードエラー数 |
| ツール | tool_call_total | Counter | ツール呼び出し総数 |
| ツール | tool_call_duration | Histogram | ツール呼び出し時間 |
| LLM | llm_token_input | Counter | 入力トークン消費量 |
| LLM | llm_token_output | Counter | 出力トークン消費量 |
| LLM | llm_request_latency | Histogram | LLMリクエストレイテンシ |
| リトライ | retry_attempts_total | Counter | リトライ総回数 |
| リトライ | retry_exhausted_total | Counter | リトライ枯渇回数 |
Micrometerメトリクス収集
@Component
public class WorkflowMetricsCollector {
private final MeterRegistry meterRegistry;
public void recordWorkflowExecution(String workflowId, Duration duration, boolean success) {
Counter.builder("workflow.execution.total")
.tag("workflow", workflowId)
.tag("success", String.valueOf(success))
.register(meterRegistry).increment();
Timer.builder("workflow.execution.duration")
.tag("workflow", workflowId)
.register(meterRegistry)
.record(duration);
}
public void recordNodeExecution(String nodeId, String nodeType,
Duration duration, boolean success) {
Counter.builder("node.execution.total")
.tag("node", nodeId)
.tag("type", nodeType)
.tag("success", String.valueOf(success))
.register(meterRegistry).increment();
Timer.builder("node.execution.duration")
.tag("node", nodeId)
.tag("type", nodeType)
.register(meterRegistry)
.record(duration);
}
public void recordTokenUsage(String model, int inputTokens, int outputTokens) {
Counter.builder("llm.token.input")
.tag("model", model)
.register(meterRegistry).increment(inputTokens);
Counter.builder("llm.token.output")
.tag("model", model)
.register(meterRegistry).increment(outputTokens);
}
}
パフォーマンス最適化チェックリスト
| 最適化項目 | 最適化前 | 最適化後 | 手法 |
|---|---|---|---|
| LLM呼び出しレイテンシ | 2.5s/回 | 0.8s/回 | セマンティックキャッシュ（同一プロンプトヒット） |
| ワークフロー起動 | 500ms | 50ms | DAG事前コンパイル + オブジェクトプール |
| チェックポイント保存 | 200ms | 30ms | 非同期バッチRedis Pipeline書き込み |
| ノード間データ渡し | シリアライズオーバーヘッド | ゼロコピー | 参照渡し + Copy-on-Write |
| 並列ノードスケジューリング | 順次提交 | 真の並列 | ForkJoinPool + WorkStealing |
| ツールSchema生成 | 毎回リフレクション | 1回 | 起動時事前生成 + キャッシュ |
セマンティックキャッシュ実装
@Component
public class SemanticCache {
private final EmbeddingModel embeddingModel;
private final VectorStore vectorStore;
@Value("${workflow.cache.similarity-threshold:0.95}")
private double similarityThreshold;
public Optional<String> get(String prompt) {
float[] embedding = embeddingModel.embed(prompt);
List<Document> results = vectorStore.similaritySearch(
SearchRequest.builder()
.query(prompt)
.topK(1)
.similarityThreshold(similarityThreshold)
.build());
if (!results.isEmpty()) {
Document doc = results.get(0);
log.info("Semantic cache hit for prompt (similarity: {})",
doc.getMetadata().get("distance"));
return Optional.of(doc.getContent());
}
return Optional.empty();
}
public void put(String prompt, String response) {
float[] embedding = embeddingModel.embed(prompt);
Document doc = new Document(prompt, response,
Map.of("embedding", embedding, "timestamp", Instant.now().toString()));
vectorStore.add(List.of(doc));
}
}
Docker Composeデプロイ設定
version: '3.8'
services:
workflow-engine:
image: toolsku/workflow-engine:1.0.0
ports:
- "8080:8080"
environment:
- SPRING_PROFILES_ACTIVE=production
- SPRING_AI_OPENAI_API_KEY=${OPENAI_API_KEY}
- REDIS_HOST=redis
- POSTGRES_HOST=postgres
depends_on:
- redis
- postgres
deploy:
resources:
limits:
memory: 2G
cpus: '4'
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/actuator/health"]
interval: 30s
timeout: 10s
retries: 3
redis:
image: redis:7-alpine
command: redis-server --maxmemory 512mb --maxmemory-policy allkeys-lru
ports:
- "6379:6379"
postgres:
image: pgvector/pgvector:pg16
environment:
POSTGRES_DB: workflow
POSTGRES_USER: workflow
POSTGRES_PASSWORD: ${DB_PASSWORD}
ports:
- "5432:5432"
prometheus:
image: prom/prometheus
volumes:
- ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml
ports:
- "9090:9090"
grafana:
image: grafana/grafana
ports:
- "3000:3000"
まとめ：自前構築 vs フレームワーク活用の意思決定マトリクス
| 次元 | 自前エンジン | LangGraph | Spring AI | Dify |
|---|---|---|---|---|
| DAGワークフロー | ✅ 完全制御 | ✅ | ❌ | ✅ |
| 状態永続化 | ✅ Redis | ⚠️ 自前必要 | ❌ | ✅ |
| Multi-Agent | ✅ 4パターン | ⚠️ 基本のみ | ❌ | ⚠️ |
| Javaエコシステム | ✅ ネイティブ | ❌ Python | ✅ | ❌ |
| オブザーバビリティ | ✅ Micrometer | ✅ LangSmith | ⚠️ | ✅ |
| 学習曲線 | 高 | 中 | 低 | 低 |
| 保守コスト | 高 | 中 | 低 | 低 |
| カスタマイズ柔軟性 | ✅ 最高 | 中 | 中 | 低 |
推奨：
- 中小企業/迅速な検証 → DifyまたはSpring AIを活用、車輪の再発明を避ける
- Javaスタック/深いカスタマイズが必要 → 自前エンジン構築、本記事のアーキテクチャをそのまま適用可能
- Pythonチーム/LangChain経験あり → LangGraph、最も成熟したエコシステム
自前エンジン構築の核心的価値は「他より優れていること」ではなく「完全に制御できること」——エージェントが内部マイクロサービス、レガシーシステム、カスタムミドルウェアと深く統合する必要がある場合、自前構築が唯一の選択肢である。
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