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Production Engineering

7 articles · Page 1 of 1
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.

Async ProcessingBatch ProcessingData Validation
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.

KubernetesMilvusProduction Engineering
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.

Distributed SystemsIdempotencyMCP
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 CallOrder Management
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 Engineeringautomationchaos engineering
0 likes · 11 min read
Mastering System Reliability: Lessons from Google, Netflix, and Meta