回滚

引言 Agent系统的发布比传统应用复杂得多——一个Prompt的微调可能导致Agent行为完全改变，一个工具的版本升级可能影响所有依赖它的Agent。传统的"停机发布"在Agent系统中不可接受，而简单的"滚动更新"也无法满足Agent系统对质量保障的高要求。 2026年，金丝雀发布 + 自动回滚已成为Agent系统的标准发布实践，但Agent系统的灰度发布有其独特的挑战和解决方案。 Agent发布的特殊性 维度 传统应用 Agent系统 变更类型 代码逻辑 Prompt/模型/工具/代码 质量评估 单元测试+集成测试 需要LLM评估+人工审核 回滚速度 秒级 秒级（代码）/分钟级（模型） 影响范围 功能正确性 对话质量、安全性、成本 监控指标 错误率、延迟 +质量评分、Token消耗、用户满意度 灰度发布策略 策略一：金丝雀发布 class CanaryReleaseManager: """金丝雀发布管理器""" def __init__(self, traffic_router, metrics_collector): self.router = traffic_router self.metrics = metrics_collector async def canary_deploy( self, new_version: str, stages: list = None ) -> bool: """渐进式金丝雀发布""" if stages is None: stages = [ {"traffic_percent": 5, "duration_minutes": 10}, {"traffic_percent": 20, "duration_minutes": 15}, {"traffic_percent": 50, "duration_minutes": 20}, {"traffic_percent": 100, "duration_minutes": 30}, ] baseline = await self.metrics.get_baseline() for stage in stages: # 调整流量分配 await self.router.set_traffic_split({ "stable": 100 - stage["traffic_percent"], "canary": stage["traffic_percent"] }) logger.info( f"Canary stage: {stage['traffic_percent']}% traffic " f"for {stage['duration_minutes']}min" ) # 等待观察期 await asyncio.sleep(stage["duration_minutes"] * 60) # 评估金丝雀指标 canary_metrics = await self.metrics.collect("canary") evaluation = self._evaluate(baseline, canary_metrics) if evaluation["action"] == "rollback": logger.warning( f"Canary failed at {stage['traffic_percent']}%: " f"{evaluation['reason']}" ) await self._rollback() return False elif evaluation["action"] == "hold": logger.info(f"Pausing canary: {evaluation['reason']}") await self._notify_human(evaluation) await self._wait_for_approval() # 所有阶段通过，完成发布 await self.router.promote_canary() return True def _evaluate(self, baseline: dict, canary: dict) -> dict: """评估金丝雀健康度""" checks = [ self._check_error_rate(baseline, canary), self._check_latency(baseline, canary), self._check_quality_score(baseline, canary), self._check_cost(baseline, canary), self._check_safety(baseline, canary), ] for check in checks: if check["status"] == "fail": return {"action": "rollback", "reason": check["reason"]} if check["status"] == "warn": return {"action": "hold", "reason": check["reason"]} return {"action": "proceed", "reason": "All checks passed"} def _check_quality_score(self, baseline: dict, canary: dict) -> dict: """质量评分检查——Agent特有的评估维度""" quality_drop = baseline["quality_score"] - canary["quality_score"] if quality_drop > 0.1: # 质量下降超过10% return { "status": "fail", "reason": f"Quality dropped {quality_drop:.1%}" } elif quality_drop > 0.05: return { "status": "warn", "reason": f"Quality dropped {quality_drop:.1%}, review needed" } return {"status": "pass"} def _check_safety(self, baseline: dict, canary: dict) -> dict: """安全检查——检测有害输出""" safety_violation_rate = canary.get("safety_violation_rate", 0) if safety_violation_rate > 0.001: # 0.1%安全违规 return { "status": "fail", "reason": f"Safety violation rate: {safety_violation_rate:.3%}" } return {"status": "pass"} 策略二：蓝绿部署 # K8s蓝绿部署配置 apiVersion: argoproj.io/v1alpha1 kind: Rollout metadata: name: agent-service spec: replicas: 10 strategy: blueGreen: activeService: agent-service-active previewService: agent-service-preview autoPromotionEnabled: false # 手动确认 scaleDownDelaySeconds: 30 prePromotionAnalysis: templates: - templateName: agent-quality-check args: - name: service-name value: agent-service-preview selector: matchLabels: app: agent-service template: metadata: labels: app: agent-service spec: containers: - name: agent image: agent/service:{{ .Values.version }} env: - name: AGENT_VERSION value: "{{ .Values.version }}" - name: MODEL_ENDPOINT value: "http://llm-service:8080/v1" 策略三：流量镜像 流量镜像是Agent系统特别适合的灰度策略——将生产流量复制一份到新版本，不影响真实用户： ...