97% 的开发者在使用 Claude Code 时会遭遇失败。原因通常不是因为 AI 不够聪明，而是陷入了“上下文窗口死亡螺旋”——实现细节挤占了架构规划，导致智能体“失忆”。

本教程将教你如何通过四层编排架构（4-Layer Orchestra Architecture），从零构建一个能够处理复杂任务、保持上下文清晰且高效协作的智能体系统。

核心理念：为什么需要编排？

单智能体模式在处理超过 10-15 个文件的修改时通常会崩溃。成功的关键在于让主智能体（编排器）保持纯粹的编排模式，绝不触碰代码实现。

我们将通过四个层级来构建这个系统：

  1. 编排层：分解任务。
  2. 上下文管理层：隔离状态。
  3. 执行层：专家分工。
  4. 验证层：集成检查。

第 1 步：创建编排器智能体 (The Orchestrator)

这是系统的“大脑”。它的唯一职责是分解任务和协调专家，绝对不能写代码。这样可以确保架构计划始终保留在其上下文窗口的最前端。

配置文件：.claude/agents/orchestrator.md

  1. # .claude/agents/orchestrator.md
  2. ---
  3. name: orchestrator
  4. description: MUST BE USED for all multi-file operations. Decomposes tasks and coordinates specialist agents.
  5. ---
  6. You are a pure orchestration agent. You NEVER write code.
  7. Your responsibilities:
  8. 1. Analyze incoming requests for complexity and dependencies
  9. 2. Decompose into atomic, parallelizable tasks
  10. 3. Assign tasks to appropriate specialists
  11. 4. Monitor progress and handle inter-agent dependencies
  12. 5. Synthesize results into coherent deliverables
  14. When you receive a request:
  15. - Map all file dependencies
  16. - Identify parallelization opportunities
  17. - Create explicit task boundaries
  18. - Define success criteria for each subtask

如何使用： 初始化时，只给编排器最小的项目上下文：

  1. # Initialize orchestrator with project context
  2. claude --agent orchestrator --context-mode minimal \
  3. "Implement WebSocket real-time notifications with Redis pub/sub"

第 2 步：构建上下文管理系统 (Context Management)

为了防止不同智能体之间的上下文污染，我们需要一个“枢纽”来管理状态。每个智能体只获取它需要的上下文，从而节省 60-70% 的 Token。

实现代码：context_manager.py

  1. # context_manager.py
  2. class AgentContextHub:
  3.     def __init__(self):
  4.         self.project_state = {
  5.             'architecture': {},  # High-level decisions
  6.             'dependencies': {},  # Inter-agent dependencies
  7.             'completions': {},   # Finished tasks
  8.             'interfaces': {},    # Contract definitions
  9.             'conflicts': []      # Detected inconsistencies
  10.         }
  12.     def register_task(self, agent_id, task_spec):
  13.         """Register task without implementation details"""
  14.         return {
  15.             'task_id': self.generate_task_id(),
  16.             'dependencies': self.extract_dependencies(task_spec),
  17.             'interfaces': self.extract_interfaces(task_spec),
  18.             'context_window': self.allocate_context_window(agent_id)
  19.         }
  21.     def handoff_protocol(self, from_agent, to_agent, artifacts):
  22.         """Structured handoff maintaining context boundaries"""
  23.         return {
  24.             'interfaces': self.project_state['interfaces'],
  25.             'relevant_completions': self.filter_completions(to_agent),
  26.             'artifacts': self.validate_artifacts(artifacts),
  27.             'constraints': self.get_agent_constraints(to_agent)
  28.         }

第 3 步：部署专业执行智能体 (Specialized Execution Agents)

专家智能体只负责特定领域的实现（如后端、前端、数据库）。它们仅加载特定技能和上下文。

示例配置：后端专家 (.claude/agents/backend-specialist.md)

  1. # .claude/agents/backend-specialist.md
  2. ---
  3. name: backend-specialist
  4. description: Use PROACTIVELY for all API, database, and server-side implementations
  5. ---
  6. You are a backend implementation specialist.
  7. Technical constraints:
  8. - Node.js 20+ with TypeScript
  9. - Express.js for routing
  10. - PostgreSQL with Prisma ORM
  11. - Error-first callback pattern
  12. - Async/await for all database operations
  14. You receive task specifications from the orchestrator. You return ONLY:
  15. 1. Implemented code
  16. 2. Interface contracts
  17. 3. Test requirements
  18. 4. Dependencies needed

如何调用： 专家智能体通过上下文枢纽获取精简后的任务信息：

  1. # Specialist receives minimal context
  2. claude --agent backend-specialist \
  3. --context-from hub:interfaces \
  4. --task "Implement WebSocket connection handler with heartbeat"

第 4 步：集成验证层 (Integration Validation)

在并行开发中，不同智能体的代码可能会发生冲突。集成验证层用于防止类型不匹配、接口冲突或竞争条件。

实现代码：integration_validator.py

  1. # integration_validator.py
  2. class IntegrationValidator:
  3.     def validate_interfaces(self, implementations):
  4.         """Ensure all interfaces align across agents"""
  5.         mismatches = []
  6.         for impl in implementations:
  7.             # Check type signatures
  8.             if not self.validate_types(impl['types'], self.canonical_types):
  9.                 mismatches.append({
  10.                     'agent': impl['agent_id'],
  11.                     'type': 'type_mismatch',
  12.                     'details': self.diff_types(impl['types'])
  13.                 })
  14.             # Validate API contracts
  15.             if not self.validate_contracts(impl['contracts']):
  16.                 mismatches.append({
  17.                     'agent': impl['agent_id'],
  18.                     'type': 'contract_violation',
  19.                     'fix': self.suggest_contract_fix(impl)
  20.                 })
  21.         return self.coordinate_fixes(mismatches) if mismatches else None
  23.     def detect_race_conditions(self, parallel_implementations):
  24.         """Identify potential race conditions in parallel code"""
  25.         # Analyzes resource access patterns
  26.         # Detects missing synchronization
  27.         # Suggests mutex/semaphore placement
  28.         pass

进阶：高级编排模式

为了让系统更加健壮，你可以实施以下几种高级模式。

模式 1：波次部署 (Wave-Based Deployment)

将智能体分批次部署，在保持并行性的同时管理上下文预算。

  1. class WaveOrchestrator:
  2.     def deploy_waves(self, tasks):
  3.         waves = []
  4.         current_wave = []
  5.         context_budget = 0
  7.         for task in tasks:
  8.             estimated_context = self.estimate_context_usage(task)
  9.             if context_budget + estimated_context > self.MAX_CONTEXT:
  10.                 waves.append(current_wave)
  11.                 current_wave = [task]
  12.                 context_budget = estimated_context
  13.             else:
  14.                 current_wave.append(task)
  15.                 context_budget += estimated_context
  17.         if current_wave:
  18.             waves.append(current_wave)
  19.         return waves
  21.     def execute_waves(self, waves):
  22.         for i, wave in enumerate(waves):
  23.             print(f"Deploying wave {i+1}/{len(waves)}")
  24.             # Parallel execution within wave
  25.             results = parallel_execute(wave)
  26.             # Synthesis between waves
  27.             self.synthesize_results(results)
  28.             # Context cleanup before next wave
  29.             self.cleanup_transient_context()

模式 2：渐进式上下文摘要 (Progressive Context Summarization)

对于长时间运行的会话，自动压缩旧的上下文信息。

  1. class ContextCompressor:
  2.     def compress_conversation(self, messages, threshold=0.8):
  3.         """Compress when approaching context limit"""
  4.         if self.context_usage() > threshold:
  5.             # Identify compressible sections
  6.             sections = self.identify_sections(messages)
  7.             for section in sections:
  8.                 if section['type'] == 'implementation_detail':
  9.                     # Compress to interface only
  10.                     compressed = self.extract_interface(section)
  11.                 elif section['type'] == 'debugging_session':
  12.                     # Compress to final fix only
  13.                     compressed = self.extract_solution(section)
  14.                 elif section['type'] == 'exploration':
  15.                     # Compress to decisions only
  16.                     compressed = self.extract_decisions(section)
  18.                 section.replace_with(compressed)
  19.         return messages

模式 3：智能体生命周期管理 (Agent Lifecycle Management)

明确定义何时生成、继续或终止一个智能体，防止资源浪费和死循环。

配置文件：.claude/commands/agent-lifecycle.md

  1. # .claude/commands/agent-lifecycle.md
  2. ---
  3. name: agent-lifecycle
  4. description: Manage agent spawning and termination
  5. ---
  6. Agent lifecycle rules:
  8. SPAWN conditions:
  9. - Task complexity exceeds single-agent threshold (>5 files)
  10. - Parallel work possible (independent modules)
  11. - Specialization needed (specific expertise required)
  13. CONTINUE conditions:
  14. - Agent maintaining <70% context usage
  15. - Making consistent progress (no loops detected)
  16. - No architectural drift from specifications
  18. TERMINATE conditions:
  19. - Three consecutive incorrect suggestions
  20. - Context usage >85% with degrading quality
  21. - Circular modifications detected (A→B→A pattern)
  22. - Task complete or blocked
  24. Termination protocol:
  25. 1. Save agent state to context hub
  26. 2. Extract completed work artifacts
  27. 3. Log termination reason
  28. 4. Reassign incomplete tasks if needed

模式 4：上下文移交协议 (The Context Handoff Protocol)

结构化的移交协议确保信息在智能体之间传递时不丢失。

  1. {
  2.   "handoff_protocol": {
  3.     "from_agent": "backend-specialist",
  4.     "to_agent": "frontend-specialist",
  5.     "timestamp": "2024-11-14T10:30:00Z",
  6.     "artifacts": {
  7.       "interfaces": {
  8.         "websocket_events": ["connection", "message", "disconnect"],
  9.         "message_types": ["operation", "presence", "acknowledgment"],
  10.         "api_endpoints": {
  11.           "GET /documents/:id": "Returns document with operations",
  12.           "POST /documents/:id/operations": "Applies new operation"
  13.         }
  14.       },
  15.       "implementation_notes": {
  16.         "critical": "Operations must be applied in timestamp order",
  17.         "optimization": "Batch operations every 100ms",
  18.         "limitation": "Max 1000 operations per document in memory"
  19.       },
  20.       "dependencies": ["ws@8.0.0", "uuid@9.0.0"],
  21.       "test_requirements": [
  22.         "Concurrent operation ordering",
  23.         "Reconnection with state recovery",
  24.         "Operation compression for large documents"
  25.       ]
  26.     },
  27.     "next_agent_context": {
  28.       "focus": "Build React components consuming these WebSocket events",
  29.       "constraints": "Maintain optimistic UI updates with rollback capability",
  30.       "available_context_budget": 8500
  31.     }
  32.   }
  33. }

总结：从现在开始行动

这种编排架构虽然前期投入（Token 和设置）稍高，但能换来 100% 的任务完成率和零架构偏离。

你的行动计划：

  1. 今天：创建 .claude/agents/orchestrator.md，定义你的编排器。
  2. 本周：添加第一个专家智能体（推荐从 test-specialist 开始，风险最低）。
  3. 下周：实现简单的上下文枢纽逻辑。

像指挥家一样指挥你的 AI，而不是像对待实习生一样盯着它们。这将彻底改变你的开发效率。