# AgentSpawn核心逻辑伪代码
class AgentSpawner:
    def __init__(self, orchestrator, agent_registry):
        self.orchestrator = orchestrator
        self.registry = agent_registry  # 当前活跃Agent池
        self.capability_map = {}       # 能力→Agent映射

    def analyze_task(self, task_context):
        """运行时分析任务需求与当前Agent能力的匹配度"""
        required_capabilities = self.orchestrator.decompose(task_context)
        gaps = []
        for cap in required_capabilities:
            if cap not in self.capability_map:
                gaps.append(cap)
        return gaps

    def spawn_expert(self, capability, task_context):
        """动态生成专家Agent"""
        expert_prompt = self.build_expert_prompt(capability, task_context)
        expert = Agent(
            system_prompt=expert_prompt,
            tools=self.select_tools(capability),
            context_window=self.allocate_context(capability)
        )
        self.registry.register(expert)
        self.capability_map[capability] = expert.id
        return expert

    def execute_with_spawn(self, task):
        gaps = self.analyze_task(task)
        for cap in gaps:
            self.spawn_expert(cap, task)
        return self.orchestrator.delegate(task, self.registry)

# DRTAG弹性伸缩核心逻辑
class DRTAGAutoscaler:
    def __init__(self, config):
        self.scale_up_threshold = config.get('scale_up_threshold', 0.8)
        self.scale_down_threshold = config.get('scale_down_threshold', 0.3)
        self.cooldown_period = config.get('cooldown', 60)  # seconds
        self.agent_pool = {}

    def measure_load(self, agent_id):
        """测量单个Agent负载"""
        metrics = {
            'queue_depth': self.get_queue_depth(agent_id),
            'avg_response_time': self.get_avg_latency(agent_id),
            'token_utilization': self.get_token_usage(agent_id)
        }
        return metrics['queue_depth'] * 0.4 + \
               metrics['avg_response_time'] * 0.3 + \
               metrics['token_utilization'] * 0.3

    def autoscale(self):
        """自动扩缩容主循环"""
        for role, agents in self.agent_pool.items():
            avg_load = sum(self.measure_load(a.id) for a in agents) / len(agents)

            if avg_load > self.scale_up_threshold:
                # 扩容：创建角色副本
                new_agent = self.clone_agent(agents[0])
                self.agent_pool[role].append(new_agent)
                self.distribute_load(role, new_agent)

            elif avg_load < self.scale_down_threshold and len(agents) > 1:
                # 缩容：回收最空闲的Agent
                idlest = min(agents, key=lambda a: self.measure_load(a.id))
                self.graceful_shutdown(idlest)
                self.agent_pool[role].remove(idlest)

graph TD
    A[任务输入] --> B{任务分析器}
    B -->|并行可分解| C[星型/网状拓扑]
    B -->|串行依赖| D[链式拓扑]
    B -->|混合复杂| E[层级拓扑]

    C --> F[5-8 Agents<br/>效率峰值]
    D --> G[2-3 Agents<br/>减少开销]
    E --> H[3-5 Agents<br/>+ 主Agent]

    F --> I{共享级别}
    G --> I
    H --> I

    I -->|工具+摘要| J[最优配置]
    I -->|全量共享| K[上下文污染]
    I -->|无共享| L[协作断裂]

    style J fill:#4CAF50,color:white
    style K fill:#f44336,color:white
    style L fill:#ff9800,color:white

# Kubernetes Agent Sandbox配置示例
apiVersion: apps.k8s.io/v1alpha1
kind: AgentPool
metadata:
  name: code-review-agents
spec:
  replicas: 3
  minReplicas: 1
  maxReplicas: 20
  template:
    spec:
      identity:
        persistentId: "code-reviewer-v2"
        scratchVolume: 1Gi
      tools:
        - name: github-api
          permissions: [read, write]
        - name: code-analyzer
          permissions: [read]
      model:
        provider: openai
        name: gpt-4o
        maxTokens: 8000
      sandbox:
        isolation: firecracker   # firecracker | gvisor | runc
        resources:
          cpu: "2"
          memory: "4Gi"
          gpu: "0"
  scaling:
    metrics:
      - type: External
        external:
          metric:
            name: agent_queue_depth
          target:
            type: AverageValue
            averageValue: "5"
    behavior:
      scaleUp:
        stabilizationWindowSeconds: 30
        policies:
          - type: Percent
            value: 100
            periodSeconds: 60
      scaleDown:
        stabilizationWindowSeconds: 300

# AgentPool核心API
from agentpool import AgentPool, PoolConfig

# 创建Agent池
pool = AgentPool(
    config=PoolConfig(
        min_size=2,          # 最小常驻Agent数
        max_size=10,         # 最大Agent数
        max_idle_time=300,   # 空闲超时（秒）
        acquire_timeout=30,  # 获取超时
        recycle_interval=3600, # 回收间隔（防止上下文污染）

        # Agent模板配置
        agent_templates={
            "researcher": AgentTemplate(
                system_prompt="你是一个研究助手...",
                tools=[web_search, arxiv_search],
                model="claude-sonnet",
                max_context=8000
            ),
            "coder": AgentTemplate(
                system_prompt="你是一个编程专家...",
                tools=[file_read, file_write, shell],
                model="gpt-4o",
                max_context=16000
            )
        }
    )
)

# 使用Agent
async with pool.acquire("researcher") as agent:
    result = await agent.run("分析最新的Agent论文")
    # 使用完毕，Agent回到池中等待复用

# 手动扩缩容
pool.scale("researcher", target=5)    # 扩容到5个
pool.scale("coder", target=1)          # 缩容到1个

# AgentBalance优化流程
class AgentBalanceOptimizer:
    def __init__(self, budget_tokens=50000):
        self.budget = budget_tokens

    def optimize(self, task_profile):
        # Phase 1: Backbone Selection
        backbone = self.select_backbone(
            task_type=task_profile.type,
            complexity=task_profile.complexity,
            available_models=[
                ('gpt-4o', 128000, 0.03),        # (model, ctx, $/1k tokens)
                ('gpt-4o-mini', 128000, 0.005),
                ('claude-haiku', 200000, 0.001),
            ]
        )

        # Phase 2: Topology Optimization
        topology = self.optimize_topology(
            backbone=backbone,
            budget=self.budget,
            communication_cost=self.estimate_comm_cost(backbone)
        )

        return {
            'agents': backbone,
            'topology': topology,
            'estimated_tokens': self.estimate_total_tokens(backbone, topology),
            'estimated_cost': self.estimate_cost(backbone, topology)
        }

    def estimate_comm_cost(self, backbone):
        """估算Agent间通信的token成本"""
        # 每条边 = system_prompt + context_transfer
        edge_cost = sum(a.context_size for a in backbone) * 0.1
        return edge_cost

graph TB
    subgraph "任务输入层"
        TASK[任务请求]
    end

    subgraph "弹性调度层"
        ANALYZER[任务分析器<br/>任务类型·复杂度·并行度]
        SCHEDULER{调度决策}

        ANALYZER --> SCHEDULER
    end

    subgraph "Agent池管理层"
        POOL[Agent Pool<br/>最小常驻·最大弹性·回收策略]
        SPAWNER[动态生成器<br/>角色模板·工具集·上下文]
        RECYCLER[回收器<br/>空闲检测·状态快照·资源释放]

        POOL --- SPAWNER
        POOL --- RECYCLER
    end

    subgraph "基础设施层"
        K8S[Kubernetes Agent Sandbox<br/>CRD·HPA·Firecracker]
        MODEL_ROUTER[模型路由网关<br/>成本·延迟·质量三角]
        CONTEXT_STORE[上下文存储<br/>Redis·Postgres·向量DB]

        K8S --- MODEL_ROUTER
        MODEL_ROUTER --- CONTEXT_STORE
    end

    subgraph "成本控制层"
        BUDGET[Token预算管理器<br/>AgentBalance拓扑优化]
        MONITOR[成本监控<br/>实时token·$消耗追踪]

        BUDGET --- MONITOR
    end

    TASK --> ANALYZER
    SCHEDULER -->|扩容| SPAWNER
    SCHEDULER -->|分配| POOL
    SCHEDULER -->|缩容| RECYCLER
    POOL --> K8S
    MONITOR -->|反馈| SCHEDULER
    BUDGET -->|约束| SPAWNER

    style SCHEDULER fill:#2196F3,color:white
    style POOL fill:#4CAF50,color:white
    style BUDGET fill:#FF9800,color:white
    style K8S fill:#9C27B0,color:white