🚰 Agentic 数据管道

# 传统ETL - 僵化
def extract_data(source_config):
    # 硬编码的字段映射
    return {
        "user_id": source["id"],
        "name": source["username"],  # 如果字段改名就崩了
    }

# Agentic Pipeline - 自适应
async def agentic_extract(agent, source):
    # Agent先理解数据结构
    schema = await agent.analyze_schema(source)
    
    # 自动推断字段映射
    mapping = await agent.infer_mapping(
        target_schema, source_schema
    )
    
    # 处理异常情况
    if mapping.confidence < 0.8:
        await agent.request_human_approval(mapping)
    
    return await agent.transform(source, mapping)

# Agentic数据清洗示例
class DataCleansingAgent:
    async def clean(self, dataset):
        issues = await self.detect_issues(dataset)
        
        for issue in issues:
            if issue.type == "missing_values":
                strategy = await self.choose_strategy(issue)
                # Agent可以选择：删除/填充/插值/标记
                dataset = await strategy.apply(dataset)
                
            elif issue.type == "outliers":
                # Agent分析异常原因
                reason = await self.analyze_outlier(issue)
                if reason == "data_entry_error":
                    dataset = await self.correct_error(issue)
                elif reason == "seasonal_pattern":
                    dataset = await self.keep_with_flag(issue)
        
        return dataset

# OpenClaw Skill: agentic-log-pipeline
"""
多服务日志收集 + 智能分析
"""

# Agent 1: 日志收集Agent
async def collect_logs(sources):
    for source in sources:
        try:
            logs = await fetch_logs(source)
            await agentic_analyze(logs)
        except Exception as e:
            # Agent自己决定处理策略
            strategy = await agent.choose_recovery_strategy(e)
            await strategy.execute()

# Agent 2: 日志分析Agent
async def agentic_analyze(logs):
    # 自动识别日志模式
    patterns = await detect_patterns(logs)
    
    # 发现异常自动告警
    if patterns.has_anomaly():
        await notify_human({
            "anomaly": patterns.anomaly,
            "suggested_fix": await agent.suggest_fix(patterns)
        })
    
    # 自动归档冷数据
    await archive_cold_data(logs)

# OpenClaw Sub-agent协作数据融合
task = """
任务：将MySQL用户数据、MongoDB订单数据、Redis会话数据融合

Agent分配：
- Agent A: MySQL数据提取 + Schema标准化
- Agent B: MongoDB数据提取 + Schema标准化  
- Agent C: Redis数据提取 + Schema标准化
- Agent D (协调者): 数据融合 + 冲突解决

每个Agent自主决策：
- 发现字段类型不匹配 → 自动转换
- 发现重复数据 → 智能去重
- 发现关联关系 → 自动JOIN
"""

sessions_spawn("data-fusion-orchestrator", task)

# OpenClaw实时数据处理Skill
from openclaw import Skill

class RealtimeDataPipeline(Skill):
    def __init__(self):
        self.agents = {
            "ingest": IngestAgent(),
            "process": ProcessAgent(),
            "sink": SinkAgent()
        }
        self.coordinator = PipelineCoordinator()
    
    async def process_stream(self, stream):
        async for event in stream:
            # Agent自主决策处理路径
            route = await self.coordinator.route(event)
            
            if route == "fast_path":
                # 简单事件直接处理
                await self.agents["process"].fast_process(event)
            elif route == "deep_analysis":
                # 复杂事件深度分析
                analysis = await self.agents["process"].deep_analyze(event)
                await self.agents["sink"].store(analysis)
            elif route == "human_review":
                # 不确定时请求人工审核
                await self.request_human_review(event)