Auto Agent System - Part 4 - 生產化之路:Langfuse 可觀測性、Docker 瘦身與 AWS 部署

AI 系統能跑,離能上線還很遠。本篇拆解 agent_auto_system 把自己推向生產的四個關鍵決策:在 executor 這個漏斗掛上 Langfuse trace(PR #19)、用 WeasyPrint 換掉 Chromium 讓 Docker image 瘦身(PR #9)、規劃 AWS ECS Fargate 部署(PR #10),以及完整的登入與 RBAC 權限系統(PR #11)。

·22 min aiengineering

Building Centralized Grafana + Prometheus Monitoring with AWS CDK: Multi-Service Observability Platform

Comprehensive guide to architecting a production-ready centralized Prometheus + Grafana monitoring platform using AWS CDK that aggregates metrics from multiple services, clusters, and infrastructure components with federation, remote storage, and advanced alerting.

·23 min engineeringarchitecture

Deploying Apache Superset at Scale: Production-Ready BI Platform with AWS CDK and ECS Fargate

Comprehensive guide to architecting a highly available, production-grade Apache Superset deployment using ECS Fargate, RDS PostgreSQL, and AWS CDK for enterprise business intelligence at scale.

·19 min engineeringarchitecture

Building Scalable WordPress on AWS ECS Fargate

Comprehensive guide to deploying production-ready WordPress on AWS ECS Fargate, exploring containerization strategies, infrastructure decisions, and scalability patterns for high-traffic content management systems.

·16 min engineeringarchitecture