推测执行无损加速：三栈工具调用实测

标准Agent Loop:
  Think(500ms) → Call Tool(2000ms) → Think(500ms) → Call Tool(2000ms)
  总计: 5000ms

Speculative Actions:
  Think(500ms) ─┬─→ [Predictor predicts tool_call_B] → Pre-execute(2000ms)
                └─→ LLM generates tool_call_A → Execute(2000ms)
                ─→ [Predictor predicts tool_call_C] → Pre-execute(2000ms)
                Verify & Accept → 总计: ~1500ms (3.4x speedup)

class SpeculativeExecutor:
    def __init__(self, llm_client, tool_registry, predictor):
        self.llm = llm_client
        self.tools = tool_registry
        self.predictor = predictor  # 轻量级预测模型

    async def step(self, messages, history):
        # 并行启动：LLM推理 + 预测器预执行
        llm_task = self.llm.generate(messages)
        pred_task = self.predictor.predict(history)

        llm_result, pred_result = await asyncio.gather(llm_task, pred_task)

        if self._matches(llm_result, pred_result):
            # 预测正确！预执行结果已就绪，直接返回
            return pred_result.cached_output
        else:
            # 预测错误，回滚预执行，走正常路径
            pred_result.rollback()
            return await self.tools.execute(llm_result.tool_call)

class PASTEExecutor:
    def __init__(self, llm, tools, pattern_db):
        self.llm = llm
        self.tools = tools
        self.patterns = pattern_db  # 从历史轨迹挖掘的模式库

    async def step(self, messages, trajectory):
        # 在LLM推理期间，同步进行模式匹配和预执行
        current_tail = trajectory[-2:]  # 最近2步工具调用
        matched_patterns = self.patterns.match(current_tail)

        speculative_results = {}
        for pattern in matched_patterns:
            if pattern.confidence > 0.85:
                next_tool = pattern.predict_next()
                speculative_results[next_tool.id] = await self.tools.pre_execute(next_tool)

        # LLM推理完成
        llm_result = await self.llm.generate(messages)

        # 验证
        if llm_result.tool_call in speculative_results:
            return speculative_results[llm_result.tool_call]
        else:
            # 回滚所有预执行
            for r in speculative_results.values():
                await r.rollback()
            return await self.tools.execute(llm_result.tool_call)

传统MCP Agent:
  50个工具定义（占用~8000 tokens）→ Agent选择工具 → 执行

Code Execution with MCP:
  1个代码执行工具 + MCP Server配置 → Agent生成代码 → 代码内调用MCP → 执行

# 传统方式：每个操作一个工具
tools = [
    {"name": "get_customer", "description": "Get customer by ID", ...},
    {"name": "search_orders", "description": "Search customer orders", ...},
    {"name": "update_address", "description": "Update customer address", ...},
    # ... 47 more tools
]

# MCP代码执行方式：一个工具搞定
tools = [
    {
        "name": "execute_code",
        "description": "Execute Python code with MCP access",
        "parameters": {"code": {"type": "string"}}
    }
]

# Agent生成的代码
response = llm.generate("Get customer 123 and their recent orders", tools)
# → generate code:
# customer = mcp.call("customer_service", "get", {"id": 123})
# orders = mcp.call("order_service", "search", {"customer_id": 123})

# conductor.yaml - 代码审查工作流
agents:
  - name: reviewer
    model: claude-opus-4-6
    system_prompt: "You are a senior code reviewer..."
  - name: security
    model: gpt-5
    system_prompt: "You are a security specialist..."
  - name: summarizer
    model: claude-sonnet-4-6
    system_prompt: "Summarize the review findings..."

workflow:
  - parallel:
      - agent: reviewer
        input: ${github.pr.diff}
        output: review_findings
      - agent: security
        input: ${github.pr.diff}
        output: security_findings
  - agent: summarizer
    input:
      - ${review_findings}
      - ${security_findings}
    output: final_summary
  - action: github.comment
    params:
      pr: ${github.pr.number}
      body: ${final_summary}

# OpenAI - parallel_tool_calls参数
response = openai.chat.completions.create(
    model="gpt-5",
    messages=[...],
    tools=[...],
    parallel_tool_calls=True  # 默认True
)
# response可能包含多个tool_calls
for tool_call in response.tool_calls:
    results.append(execute(tool_call))

# Anthropic - 天然支持多tool_use
response = anthropic.messages.create(
    model="claude-opus-4-6",
    messages=[...],
    tools=[...]
)
# response.content可能包含多个tool_use block
for block in response.content:
    if block.type == "tool_use":
        results.append(execute(block))

# Google Gemini - function_calling支持多调用
response = gemini.generate_content(
    contents=[...],
    tools=[...],
)
# response可能包含多个function_call

graph TD
    A[第1层: 超时控制] --> B[第2层: 指数退避重试]
    B --> C[第3层: 工具级熔断器]
    C --> D[第4层: Provider级熔断器]
    D --> E[第5层: 能力级降级]

    A -.- A1["每个工具调用设置超时<br/>默认30s，写操作60s"]
    B -.- B1["指数退避 + 抖动<br/>最多3次重试"]
    C -.- C1["单工具连续失败>5次<br/>打开熔断器30s"]
    D -.- D1["单Provider连续失败>10次<br/>切换备用Provider"]
    E -.- E1["核心能力降级<br/>搜索→缓存结果返回"]

    style A fill:#e1f5fe
    style B fill:#b3e5fc
    style C fill:#81d4fa
    style D fill:#4fc3f7
    style E fill:#29b6f6

class ToolResilienceStack:
    def __init__(self):
        self.timeouts = {"read": 15, "write": 30, "external": 60}
        self.circuit_breakers = {}  # per-tool
        self.provider_breaker = CircuitBreaker(threshold=10)
        self.fallback_chain = {"search": "cached_search", "execute": "dry_run"}

    async def call_tool(self, tool_name, params):
        # L1: 超时
        timeout = self.timeouts.get(tool_name.type, 30)
        try:
            result = await asyncio.wait_for(
                self._execute_with_retry(tool_name, params),
                timeout=timeout
            )
            return result
        except ToolTimeoutError:
            pass  # fall through to circuit breaker check

    async def _execute_with_retry(self, tool_name, params):
        # L3: 工具级熔断器
        if tool_name in self.circuit_breakers:
            cb = self.circuit_breakers[tool_name]
            if cb.is_open:
                raise CircuitOpenError(tool_name)

        # L2: 指数退避重试
        for attempt in range(3):
            try:
                return await tool_name.execute(params)
            except RetryableError as e:
                delay = (2 ** attempt) * 0.5 + random.uniform(0, 0.5)
                await asyncio.sleep(delay)
            except NonRetryableError:
                self.circuit_breakers.setdefault(tool_name, CircuitBreaker()).record_failure()
                raise

graph TD
    START[工具调用加速需求] --> Q1{轨迹是否≥5步?}
    Q1 -->|否| SIMPLE[使用标准并行调用<br/>三厂商原生支持即可]
    Q1 -->|是| Q2{工具调用是否高度重复?}

    Q2 -->|是| Q3{是否有历史轨迹数据?}
    Q3 -->|是| PASTE[使用PASTE<br/>挖掘模式+推测执行]
    Q3 -->|否| SPEC[使用Speculative Actions<br/>通用预测器+验证]

    Q2 -->|否| Q4{工作流是否可预定义?}
    Q4 -->|是| CONDUCTOR[使用Conductor<br/>YAML确定性编排]
    Q4 -->|否| Q5{工具数量是否>20?}

    Q5 -->|是| MCP_CE[MCP代码执行<br/>减少工具数量]
    Q5 -->|否| STANDARD[标准Agent Loop<br/>+并行调用+五层防护]

    style PASTE fill:#c8e6c9
    style SPEC fill:#c8e6c9
    style CONDUCTOR fill:#bbdefb
    style MCP_CE fill:#fff9c4
    style SIMPLE fill:#f3e5f5
    style STANDARD fill:#ffe0b2

Week 1-2: 标准Agent Loop + 原生并行调用
  ↓ 验证工具选择准确率 > 95%
Week 3-4: 叠加五层防护栈（超时→重试→熔断→切换→降级）
  ↓ 验证工具调用可靠性 > 99.9%
Week 5-6: 采集轨迹数据，分析模式覆盖率
  ↓ 如果模式覆盖率 > 70%，部署PASTE
  ↓ 如果模式覆盖率 < 70%，部署Speculative Actions
Week 7-8: A/B测试加速效果 vs 无损保证
  ↓ 验证端到端加速 > 1.5x，准确率无损
Week 9+: 全量上线 + 持续监控预测准确率