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Production Engineering

16 articles · Page 1 of 1
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 28, 2026 · Backend Development

From Code Context to 100B Docs: Qoder's Production Vector Retrieval with Elasticsearch

Alibaba's Qoder team details how they built a production-grade vector retrieval system for Agentic Coding using Alibaba Cloud Elasticsearch, handling 100B+ documents with per-codebase slices for multi-tenant isolation, achieving 14ms P95 latency, 2GB/s write throughput, and ~0.016 yuan monthly cost per codebase, while sharing indexing, query pipeline, observability, and future unified retrieval plans.

Agentic CodingAlibaba Cloud ElasticsearchCost Optimization
0 likes · 14 min read
From Code Context to 100B Docs: Qoder's Production Vector Retrieval with Elasticsearch
Cloud Architecture
Cloud Architecture
Oct 20, 2026 · Backend Development

Spring AI + Milvus: Enterprise RAG Production Engineering – Verifiable Knowledge, Permissions, Capacity & Recovery

This article presents a production-grade RAG engineering methodology using Spring AI and Milvus, covering version baselines, capacity planning, security filters, schema design, retrieval quality, prompt injection defense, knowledge versioning, Milvus topology, concurrency control, caching, SSE streaming, observability, fault injection testing, and a minimal first-week delivery checklist.

Capacity PlanningMilvusMulti-tenancy
0 likes · 42 min read
Spring AI + Milvus: Enterprise RAG Production Engineering – Verifiable Knowledge, Permissions, Capacity & Recovery
Coder Trainee
Coder Trainee
Oct 18, 2026 · Artificial Intelligence

From Demo to Production: 5 Pillars for Enterprise-Grade AI Agents

This final article in the AI Agent development series details five pillars for production-ready agents—evaluation systems, observability, safety guardrails, cost/latency optimization, and deployment/operations—illustrated with real incidents, code examples, and a 30-item checklist.

AI agentsCost OptimizationEvaluation Systems
0 likes · 31 min read
From Demo to Production: 5 Pillars for Enterprise-Grade AI Agents
Ray's Galactic Tech
Ray's Galactic Tech
Oct 14, 2026 · Backend Development

From Chatbot to AI Agent: Why a Single While Loop Fails in Production

The article details the first week of the Aegis project, demonstrating why a naive while-loop agent loop fails in production and how to replace it with deterministic contracts, a finite state machine, bounded execution budgets, server-side tool authorization, and reproducible failure testing using local mocks.

AI agentConcurrency ControlGo
0 likes · 25 min read
From Chatbot to AI Agent: Why a Single While Loop Fails in Production
DeWu Technology
DeWu Technology
Oct 14, 2026 · Backend Development

Taming LLMs with Deterministic Workflows: Dynamic Rule Engine for Asset Acceptance

Dewu Technology shares a production system that uses LLMs for asset acceptance verification across dozens of expense types with frequently changing rules, replacing hard-coded logic with a configurable rule engine, deterministic workflow orchestration, multi-model voting, and a closed-loop quality system with backtesting gates and human feedback.

LLMProduction Engineeringasset acceptance
0 likes · 43 min read
Taming LLMs with Deterministic Workflows: Dynamic Rule Engine for Asset Acceptance
Ray's Galactic Tech
Ray's Galactic Tech
Oct 11, 2026 · Backend Development

Building Production-Grade Text-to-SQL: From Natural Language to Trustworthy Data Analysis

The article presents a complete engineering framework for production Text-to-SQL systems, covering semantic contracts, metadata retrieval, controlled query compilation, security enforcement, execution budgets, evaluation, and phased rollout, using a "East China weekly payment GMV YoY" case study to demonstrate how to deliver trustworthy, auditable analytical answers rather than just executable SQL.

Execution BudgetMetadata RetrievalProduction Engineering
0 likes · 45 min read
Building Production-Grade Text-to-SQL: From Natural Language to Trustworthy Data Analysis
SpringMeng
SpringMeng
Oct 8, 2026 · Backend Development

Spring AI Alibaba Still Active: Why Java Remains Viable for Production AI Systems

The article debunks rumors that Spring AI Alibaba has stopped development, citing recent releases, and argues Java's strength in AI lies not in model training but in integrating AI capabilities into production enterprise systems through RAG, tool calling, and robust engineering practices.

AI integrationJavaLangChain4j
0 likes · 11 min read
Spring AI Alibaba Still Active: Why Java Remains Viable for Production AI Systems
Xiaolin Talks Programming
Xiaolin Talks Programming
Oct 1, 2026 · Backend Development

Spring Boot + Timefold Solver: Solving Complex Scheduling & Routing with Constraint Optimization

This article details integrating Timefold Solver with Spring Boot to tackle NP-hard optimization problems like employee scheduling and vehicle routing, covering domain modeling, constraint streams, incremental solving, score types, REST APIs, tuning strategies, production validation, and real-world pitfalls with concrete code examples and a 230-employee case study.

Constraint OptimizationConstraint StreamsEmployee Scheduling
0 likes · 24 min read
Spring Boot + Timefold Solver: Solving Complex Scheduling & Routing with Constraint Optimization
Xiaolin Talks Programming
Xiaolin Talks Programming
Sep 18, 2026 · Backend Development

Engineering Large-Scale Excel Imports: Spring Boot + EasyExcel Streaming & Task Isolation

This article details a production-ready engineering approach for importing hundreds of thousands of Excel rows using Spring Boot and EasyExcel, covering streaming parsing, batch validation, async task isolation, idempotency, error reporting, and operational concerns like logging and temp file cleanup.

Batch ProcessingEasyExcelIdempotency
0 likes · 23 min read
Engineering Large-Scale Excel Imports: Spring Boot + EasyExcel Streaming & Task Isolation
Cloud Architecture
Cloud Architecture
Sep 13, 2026 · Backend Development

Production-Grade RAG with Spring AI: Verifiable, Rollbackable, Auditable Knowledge Base

This article details a production-ready customer service knowledge base built with Spring AI 2.0.1 and Milvus, covering immutable index versioning, tenant-isolated retrieval with parameterized filters, deterministic chunk IDs, idempotent ingestion pipelines, dual-index blue-green deployments, and comprehensive observability with automated rollback triggers.

KubernetesMilvusObservability
0 likes · 27 min read
Production-Grade RAG with Spring AI: Verifiable, Rollbackable, Auditable Knowledge Base
Cloud Architecture
Cloud Architecture
Sep 7, 2026 · Backend Development

Why a Single Timeout Spawned Two Risk Reviews: Production MCP Server Patterns

The article analyzes a timeout-induced duplicate risk review incident, then presents a comprehensive production-grade MCP server design covering stateless protocol alignment, schema validation, dual-key idempotency (request_key + business_key), UNKNOWN state machine, recovery workers, MCP Tasks integration, concurrency control, observability, and fault-injection testing to ensure exactly-once business effects.

Concurrency ControlIdempotencyMCP
0 likes · 27 min read
Why a Single Timeout Spawned Two Risk Reviews: Production MCP Server Patterns
Ray's Galactic Tech
Ray's Galactic Tech
Jul 13, 2026 · Artificial Intelligence

From 22% Error Rate to 1.3%: How I Made an AI Agent Reliably Handle Order Queries and Refunds

This article analyses why Function Call demos often break in production, proposes a four‑plane architecture (control, execution, state, governance), details tool gating, idempotency, state‑machine modeling, observability and evaluation, and shows how these steps reduced the error rate of an order‑query and refund agent from 22% to 1.3%.

AI agentFunction CallProduction Engineering
0 likes · 58 min read
From 22% Error Rate to 1.3%: How I Made an AI Agent Reliably Handle Order Queries and Refunds
inShocking
inShocking
Apr 23, 2026 · Artificial Intelligence

From Chatty to Capable: Key Challenges and Solutions for Deploying AI Agents in Production

The article identifies five often‑overlooked engineering pitfalls—unstable model output, fragile tool chains, memory loss, multi‑tenant interference, and uncontrolled autonomy—and provides concrete validation, tool‑tiering, external memory, isolation, and risk‑based execution strategies to reliably move AI agents from demo to production.

AI agentsLLM reliabilityMulti-tenant Isolation
0 likes · 11 min read
From Chatty to Capable: Key Challenges and Solutions for Deploying AI Agents in Production
Yunqi AI+
Yunqi AI+
Apr 21, 2026 · Artificial Intelligence

What We Learned Building Production‑Grade AI Agents: A Retrospective

The article reviews a year of production‑grade AI agent deployments, revealing that engineering challenges—data handling, rule governance, workflow integration, context quality, and clear boundaries—are far more critical than model performance for successful real‑world adoption.

AI agentsProduction Engineeringprompt engineering
0 likes · 9 min read
What We Learned Building Production‑Grade AI Agents: A Retrospective
DevOps Coach
DevOps Coach
Dec 29, 2025 · Operations

Mastering System Reliability: Lessons from Google, Netflix, and Meta

Learn how Google, Netflix, and Meta pioneered modern reliability practices—SRE’s data‑driven metrics, Netflix’s chaos engineering, and Meta’s self‑healing automation—and get a step‑by‑step handbook to apply these concepts, avoid common traps, and build resilient systems at any scale.

Production EngineeringSite Reliability Engineeringautomation
0 likes · 11 min read
Mastering System Reliability: Lessons from Google, Netflix, and Meta