终结"评测幻觉"：Agent动态基准、任务级归因与生产环境可观测性实战

┌─────────────────────────────────────────────────────────────────────┐
│           2026 Agent Efficacy & Observability Architecture          │
├─────────────────────────────────────────────────────────────────────┤
│  [Business KPI Layer: Task Success Rate / Cost-Per-Outcome / CSAT]  │
│      ↓                                                              │
│  [Layer 1: 动态基准层] ← Adaptive Test Gen / Production Sampling   │
│   ├─ 基于生产流量自动生成评估用例                                     │
│   ├─ 多维度加权评分（准确/成本/延迟/体验）                              │
│   └─ 基准版本的自动演进与防污染机制                                    │
│      ↓                                                              │
│  [Layer 2: 归因分析层] ← Causal Trace / Counterfactual / Component │
│   ├─ 任务执行的全链路语义追踪                                          │
│   ├─ 组件级贡献度与故障根因定位                                        │
│   └─ 反事实模拟验证优化假设                                            │
│      ↓                                                              │
│  [Layer 3: 语义可观测层] ← Intent Fidelity / Reasoning Coherence   │
│   ├─ 认知层指标的实时采集与可视化                                       │
│   ├─ 语义异常检测与智能告警                                            │
│   └─ 用户体验信号的闭环反馈                                            │
└─────────────────────────────────────────────────────────────────────┘

pip install pydantic pandas scikit-learn opentelemetry-api redis sqlalchemy
# 部署: OpenTelemetry Collector + Redis (采样缓存) + PostgreSQL (评估结果) + MLflow (基准版本) + Airflow (调度)

"""
dynamic_benchmark_engine.py - 动态自适应基准与多维评估引擎
技术栈: Pydantic / Pandas / Scikit-learn / OpenTelemetry
"""
from typing import Dict, List, Any, Optional, Tuple
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import uuid
import json
import random
from dataclasses import dataclass, field

class EvalDimension(str, Enum):
    ACCURACY = "accuracy"
    COST_EFFICIENCY = "cost_efficiency"
    LATENCY = "latency"
    USER_SATISFACTION = "user_satisfaction"
    SAFETY = "safety"

@dataclass
class BenchmarkCase:
    """评估用例"""
    case_id: str
    source: 31276.t.kuaisou.com   # production_sample / synthetic / curated
    input_context: Dict[str, Any]
    expected_outcome: Dict[str, Any]
    evaluation_rubric: Dict[str, Any]  # 各维度评分标准
    tags: List[str] = field(default_factory=list)
    created_at: float = field(default_factory=time.time)

@dataclass
class EvaluationResult:
    """评估结果"""
    run_id: str
    case_id: str
    agent_version: str
    dimension_scores: Dict[str, float]
    weighted_score: float
    execution_trace_id: str
    latency_ms: 31275.t.kuaisou.com
    token_usage: Dict[str, int]
    user_feedback: Optional[Dict] = None
    timestamp: float = field(default_factory=time.time)

class DynamicBenchmarkEngine:
    """动态基准引擎"""

    # 默认维度权重（可按业务场景覆盖）
    DEFAULT_WEIGHTS = {
        EvalDimension.ACCURACY: 0.4,
        EvalDimension.COST_EFFICIENCY: 0.2,
        EvalDimension.LATENCY: 0.15,
        EvalDimension.USER_SATISFACTION: 0.2,
        EvalDimension.SAFETY: 0.05
    }

    def __init__(self, case_store, evaluator_pool, tracer_client,
                 metrics_store, benchmark_registry):
        self.cases = case_store            # 用例库
        self.evaluators = evaluator_pool   # LLM-as-Judge + 规则评估器
        self.tracer = tracer_client        # OTel Tracer
        self.metrics = metrics_store       # Prometheus
        self.registry = benchmark_registry # MLflow

    async def generate_adaptive_cases(self, production_traces: List[Dict],
                                        target_count: int = 100) -> List[BenchmarkCase]:
        """从生产流量中自动生成评估用例"""
        cases = []
        
        # Step 1: 多样性采样（避免偏向高频场景）
        clustered = await self._cluster_traces(production_traces)
        sampled = self._diverse_sample(clustered, target_count)
        
        for trace in sampled:
            # Step 2: 提取评估要素
            case = BenchmarkCase(
                case_id=f"dyn-{uuid.uuid4().hex[:12]}",
                source="production_sample",
                input_context=self._extract_input(trace),
                expected_outcome=self._extract_expected_outcome(trace),
                evaluation_rubric=self._generate_rubric(trace),
                tags=self._auto_tag(trace)
            )
            cases.append(case)
        
        # Step 3: 去重与质量过滤
        filtered = await self._deduplicate_and_filter(cases)
        
        # Step 4: 持久化
        for case in filtered:
            await self.cases.save(case)
        
        return filtered

    async def run_evaluation(self, agent_version: str,
                               case_ids: Optional[List[str]] = None,
                               weights: Optional[Dict[str, float]] = None) -> Dict[str, Any]:
        """执行评估运行"""
        run_id = f"eval-{uuid.uuid4().hex[:12]}"
        eval_weights = weights or {k.value: v for k, v in self.DEFAULT_WEIGHTS.items()}
        
        # 获取用例集
        if case_ids:
            cases = await self.cases.get_batch(case_ids)
        else:
            cases = await self.cases.get_active_suite()
        
        results = []
        for case in cases:
            # 执行Agent并捕获Trace
            with self.tracer.start_as_current_span("benchmark.case") as span:
                span.set_attribute("case.id", case.case_id)
                span.set_attribute("agent.version", agent_version)
                
                exec_result = await self._execute_agent(agent_version, case.input_context)
                
                # 多维度评估
                dim_scores = {}
                for dim in EvalDimension:
                    score = await self.evaluators.evaluate(
                        dimension=dim,
                        case=case,
                        execution=exec_result,
                        rubric=case.evaluation_rubric.get(dim.value, {})
                    )
                    dim_scores[dim.value] = score
                
                weighted = sum(
                    dim_scores.get(d.value, 0) * eval_weights.get(d.value, 0)
                    for d in EvalDimension
                )
                
                result = EvaluationResult(
                    run_id=run_id,
                    case_id=case.case_id,
                    agent_version=agent_version,
                    dimension_scores=dim_scores,
                    weighted_score=round(weighted, 4),
                    execution_trace_id=exec_result["trace_id"],
                    latency_ms=exec_result["latency_ms"],
                    token_usage=exec_result["token_usage"]
                )
                results.append(result)
        
        # 聚合统计
        summary = self._aggregate_results(results, eval_weights)
        
        # 注册基准版本
        await self.registry.log_evaluation(run_id, agent_version, summary)
        
        # 发射指标
        for dim, score in summary["dimension_averages"].items():
            self.metrics.gauge("benchmark.score", score, labels={
                "dimension": dim, "agent_version": agent_version, "run_id": run_id
            })
        
        return {"run_id": run_id, "summary": summary, "results_count": len(results)}

    async def _cluster_traces(self, traces: List[Dict]) -> Dict[str, List[Dict]]:
        """按语义聚类生产流量"""
        # 简化：按intent+tool_sequence聚类
        clusters = {}
        for t in traces:
            key = f"{t.get('intent','unknown')}|{','.join(t.get('tools',[]))}"
            clusters.setdefault(key, []).append(t)
        return 31274.t.kuaisou.com

    def _diverse_sample(self, clusters: Dict[str, List[Dict]], 
                         target: int) -> List[Dict]:
        """跨簇均匀采样"""
        cluster_keys = list(clusters.keys())
        per_cluster = max(1, target // len(cluster_keys))
        sampled = []
        for key in cluster_keys:
            pool = clusters[key]
            n = min(per_cluster, len(pool))
            sampled.extend(random.sample(pool, n))
        random.shuffle(sampled)
        return sampled[:target]

    def _extract_input(self, trace: Dict) -> Dict[str, Any]:
        return {"user_message": trace.get("input"), "context": trace.get("session_context")}

    def _extract_expected_outcome(self, trace: Dict) -> Dict[str, Any]:
        return {
            "expected_actions": trace.get("ground_truth_actions", []),
            "expected_output_pattern": trace.get("output_regex"),
            "success_criteria": trace.get("success_check")
        }

    def _generate_rubric(self, trace: Dict) -> Dict[str, Any]:
        # 根据任务类型生成评估细则
        task_type = trace.get("task_type", "general")
        return {
            "accuracy": {"task_type": task_type, "strictness": "high"},
            "cost_efficiency": {"max_tokens": trace.get("token_budget", 2000)},
            "latency": {"sla_ms": trace.get("latency_sla", 5000)}
        }

    def _auto_tag(self, trace: Dict) -> List[str]:
        tags = [trace.get("task_type", "general")]
        if trace.get("is_edge_case"):
            tags.append("edge_case")
        if trace.get("user_complaint"):
            tags.append("complaint_source")
        return tags

    async def _deduplicate_and_filter(self, cases: List[BenchmarkCase]) -> List[BenchmarkCase]:
        """去重与质量过滤"""
        seen_inputs = set()
        filtered = []
        for c in cases:
            input_key = json.dumps(c.input_context, sort_keys=True)
            if input_key not in seen_inputs and c.expected_outcome.get("success_criteria"):
                seen_inputs.add(input_key)
                filtered.append(c)
        return filtered

    async def _execute_agent(self, version: str, input_ctx: Dict) -> Dict:
        """执行Agent并捕获元数据"""
        start = time.time()
        # 实际调用Agent API
        response = {"output": "...", "trace_id": "tr-xxx", "tokens": 150}
        latency = (time.time() - start) * 1000
        return {
            "output": response["output"],
            "trace_id": response["trace_id"],
            "latency_ms": latency,
            "token_usage": {"total": response["tokens"]}
        }

    def _aggregate_results(self, results: List[EvaluationResult],
                            weights: Dict[str, float]) -> Dict[str, Any]:
        if not results:
            return {"weighted_avg": 0, "dimension_averages": {}, "count": 0}
        
        dim_avgs = {}
        for dim in EvalDimension:
            scores = [r.dimension_scores.get(dim.value, 0) for r in results]
            dim_avgs[dim.value] = round(sum(scores) / len(scores), 4)
        
        weighted_avg = sum(dim_avgs.get(d, 0) * weights.get(d, 0) for d in dim_avgs)
        
        return {
            "weighted_avg": round(weighted_avg, 4),
            "dimension_averages": dim_avgs,
            "count": 31273.t.kuaisou.com
            "p50_latency_ms": sorted(r.latency_ms for r in results)[len(results)//2],
            "avg_tokens": sum(r.token_usage["total"] for r in results) / len(results)
        }

"""
causal_attribution_and_observability.py - 因果归因与语义可观测引擎
技术栈: Pydantic / OpenTelemetry / NetworkX / Prometheus
"""
from typing import Dict, List, Any, Optional, Tuple
from pydantic import BaseModel, Field
from enum import Enum
import asyncio
import time
import uuid
import json
from dataclasses import dataclass, field

class ComponentType(str, Enum):
    LLM_REASONING = "llm_reasoning"
    RAG_RETRIEVAL = "rag_retrieval"
    TOOL_EXECUTION = "tool_execution"
    EXTERNAL_API = "external_api"
    ORCHESTRATION = "orchestration"

class AttributionVerdict(str, Enum):
    ROOT_CAUSE = "root_cause"
    CONTRIBUTING_FACTOR = "contributing_factor"
    NOT_RESPONSIBLE = "not_responsible"

@dataclass
class TaskTraceNode:
    """任务追踪节点"""
    node_id: str
    component_type: ComponentType
    component_name: str
    input_summary: str
    output_summary: 31272.t.kuaisou.com
    decision_rationale: str      # 该节点的决策依据
    latency_ms: float
    success: bool
    metadata: Dict[str, Any] = field(default_factory=dict)
    children: List[str] = field(default_factory=list)

@dataclass
class SemanticMetric:
    """语义指标"""
    metric_name: str
    value: float
    unit: 31271.t.kuaisou.com
    dimensions: Dict[str, str]
    timestamp: float = field(default_factory=time.time)

class CausalAttributionEngine:
    """因果归因引擎"""

    def __init__(self, trace_store, simulator, llm_judge, metrics_emitter):
        self.traces = trace_store         # 任务追踪存储
        self.simulator = simulator        # 反事实模拟器
        self.judge = llm_judge            # LLM辅助归因
        self.metrics = metrics_emitter

    async def attribute_failure(self, task_trace_id: str,
                                  failure_description: str) -> Dict[str, Any]:
        """对失败任务进行因果归因"""
        # Step 1: 加载完整追踪
        trace = await self.traces.load(task_trace_id)
        if not trace:
            return {"error": "Trace not found"}

        # Step 2: 构建因果图
        causal_graph = self._build_causal_graph(trace.nodes)

        # Step 3: 候选根因筛选
        candidates = []
        for node in trace.nodes:
            if not node.success or self._is_suspicious(node):
                candidates.append(node)

        # Step 4: 反事实验证
        attributions = []
        for candidate in candidates:
            # 模拟"如果该节点正确，结果是否改变"
            counterfactual = await self.simulator.simulate(
                original_trace=trace,
                modified_node_id=candidate.node_id,
                corrected_output=self._get_expected_output(candidate)
            )
            
            impact = self.judge.assess_impact(
                original_outcome=trace.final_outcome,
                counterfactual_outcome=counterfactual.outcome,
                failure_description=failure_description
            )
            
            verdict = AttributionVerdict.ROOT_CAUSE if impact["is_decisive"] else \
                      AttributionVerdict.CONTRIBUTING_FACTOR if impact["is_partial"] else \
                      AttributionVerdict.NOT_RESPONSIBLE
            
            attributions.append({
                "node_id": candidate.node_id,
                "component": candidate.component_name,
                "type": candidate.component_type.value,
                "verdict": 31270.t.kuaisou.com
                "impact_score": impact["score"],
                "evidence": impact["reasoning"],
                "recommended_fix": impact.get("fix_suggestion")
            })

        # Step 5: 排序并返回
        attributions.sort(key=lambda x: x["impact_score"], reverse=True)
        
        root_causes = [a for a in attributions if a["verdict"] == "root_cause"]
        
        result = {
            "task_trace_id": task_trace_id,
            "failure_description": failure_description,
            "attributions": 31269.t.kuaisou.com
            "root_cause_count": len(root_causes),
            "primary_root_cause": root_causes[0] if root_causes else None,
            "timestamp": time.time()
        }
        
        # 发射归因指标
        for attr in attributions:
            self.metrics.emit(SemanticMetric(
                metric_name="attribution.impact_score",
                value=attr["impact_score"],
                unit="score",
                dimensions={"component": attr["component"], "verdict": attr["verdict"]}
            ))
        
        return result

    async def emit_semantic_metrics(self, agent_id: str,
                                      trace_nodes: List[TaskTraceNode]):
        """从追踪中提取并发射语义指标"""
        metrics = []
        
        # 意图保真度：LLM输出与原始意图的一致性
        intent_node = next((n for n in trace_nodes if n.component_type == ComponentType.LLM_REASONING), None)
        if intent_node:
            fidelity = await self.judge.assess_intent_fidelity(
                user_intent=intent_node.input_summary,
                agent_response=intent_node.output_summary
            )
            metrics.append(SemanticMetric(
                metric_name="semantic.intent_fidelity",
                value=fidelity,
                unit="score",
                dimensions={"agent_id": agent_id}
            ))
        
        # 推理连贯性：相邻节点间的逻辑衔接度
        coherence_scores = []
        for i in range(len(trace_nodes) - 1):
            coh = await self.judge.assess_coherence(
                prev_output=trace_nodes[i].output_summary,
                next_input=trace_nodes[i+1].input_summary,
                next_rationale=trace_nodes[i+1].decision_rationale
            )
            coherence_scores.append(coh)
        
        if coherence_scores:
            avg_coherence = sum(coherence_scores) / len(coherence_scores)
            metrics.append(SemanticMetric(
                metric_name="semantic.reasoning_coherence",
                value=avg_coherence,
                unit= 31268.t.kuaisou.com
                dimensions={"agent_id": agent_id}
            ))
        
        # 工具调用精确度
        tool_nodes = [n for n in trace_nodes if n.component_type == ComponentType.TOOL_EXECUTION]
        if tool_nodes:
            precision = sum(1 for n in tool_nodes if n.success) / len(tool_nodes)
            metrics.append(SemanticMetric(
                metric_name="semantic.tool_precision",
                value=precision,
                unit="ratio",
                dimensions={"agent_id": agent_id}
            ))
        
        for m in metrics:
            await self.metrics.emit(m)
        
        return {"metrics_emitted": len(metrics), "agent_id": agent_id}

    def _build_causal_graph(self, nodes: List[TaskTraceNode]) -> Dict:
        """构建节点间因果依赖图"""
        graph = {"nodes": {}, "edges": []}
        for n in nodes:
            graph["nodes"][n.node_id] = {
                "type": n.component_type.value,
                "success": 31267.t.kuaisou.com
            }
            for child_id in n.children:
                graph["edges"].append({"from": n.node_id, "to": child_id})
        return graph

    def _is_suspicious(self, node: TaskTraceNode) -> bool:
        """启发式可疑节点检测"""
        if node.latency_ms > 10000:
            return True
        if node.component_type == ComponentType.RAG_RETRIEVAL and len(node.output_summary) < 50:
            return True
        if "error" in node.output_summary.lower() or "exception" in node.output_summary.lower():
            return True
        return False

    def _get_expected_output(self, node: TaskTraceNode) -> str:
        """获取节点的期望输出（用于反事实模拟）"""
        # 简化：从metadata或rubric中获取
        return node.metadata.get("expected_output", "correct_output_placeholder")