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Ray's Galactic Tech

Practice together, never alone. We cover programming languages, development tools, learning methods, and pitfall notes. We simplify complex topics, guiding you from beginner to advanced. Weekly practical content—let's grow together!

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Latest from Ray's Galactic Tech

100 recent articles max

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

Ray's Galactic Tech

Jul 13, 2026 · Artificial Intelligence

When AI Agents Meet Cloud‑Native: Practical Multi‑Agent Orchestration for High‑Concurrency Scenarios

The article explains why naïve multi‑agent demos fail in production, defines the core concepts of Task, Step, Agent Role and Event, proposes a four‑plane cloud‑native architecture, shows concrete Go and Python code, and provides detailed guidance on state machines, reliability, observability, security and budget governance for building scalable, production‑grade AI agent systems.

AI AgentsCloud NativeKubernetes

0 likes · 36 min read

When AI Agents Meet Cloud‑Native: Practical Multi‑Agent Orchestration for High‑Concurrency Scenarios

Ray's Galactic Tech

Apr 27, 2026 · Artificial Intelligence

Using AI to Auto‑Generate Forms: Production‑Ready Low‑Code Form Generation with Spring AI Alibaba ReactAgent

The article presents a production‑grade solution that lets users describe a form in natural language, then uses a Spring AI Alibaba ReactAgent powered by a ReAct reasoning loop to retrieve templates, validate fields, generate layout, enforce governance, and finally emit a versioned JSON schema ready for deployment.

ObservabilityReActReActAgent

0 likes · 29 min read

Using AI to Auto‑Generate Forms: Production‑Ready Low‑Code Form Generation with Spring AI Alibaba ReactAgent

Ray's Galactic Tech

Apr 27, 2026 · Backend Development

Java Engineer’s Complete Guide to Enterprise LLM Apps: LLM, Agent, RAG & Skill

This article walks Java engineers through building production‑grade enterprise AI assistants, explaining the roles of LLM, RAG, Agent and Skill, detailing a layered architecture, best‑practice code samples, deployment strategies, observability, security and cost‑control considerations.

AgentJavaLLM

0 likes · 37 min read

Java Engineer’s Complete Guide to Enterprise LLM Apps: LLM, Agent, RAG & Skill

Ray's Galactic Tech

Apr 26, 2026 · Cloud Native

Kubernetes Networking Unpacked: How a Service Timeout Reveals iptables‑CNI Collaboration

A real‑world Service timeout in a high‑traffic e‑commerce cluster exposed a saturated conntrack table, prompting a step‑by‑step dissection of Pods, Services, iptables, conntrack, CNI plugins, DNS and NetworkPolicy, and culminating in concrete production‑grade remediation tactics.

CNIKubernetesService

0 likes · 28 min read

Kubernetes Networking Unpacked: How a Service Timeout Reveals iptables‑CNI Collaboration

Ray's Galactic Tech

Apr 26, 2026 · Backend Development

Dissecting MCP Protocol: Scaling Java Microservices for AI‑Native Tooling

This article analyzes the Model Context Protocol (MCP), detailing its architecture, JSON‑RPC extensions, Streamable HTTP transport, and governance layers, and demonstrates how to transform high‑traffic Java microservices into a secure, observable AI‑native capability layer using an independent MCP gateway, tooling standards, and production‑grade implementations.

AI-nativeGatewayJava

0 likes · 46 min read

Dissecting MCP Protocol: Scaling Java Microservices for AI‑Native Tooling

Ray's Galactic Tech

Apr 25, 2026 · Artificial Intelligence

Mastering Spring AI MCP: Bidirectional Communication, Four Providers, Sampling Callbacks, and Dual‑Mode Deployment

This article explains why traditional function‑calling is insufficient for production AI services and shows how Spring AI's Model Context Protocol (MCP) introduces bidirectional communication, addressable resources, parameterized prompts, tool orchestration, and server‑initiated sampling, providing a complete roadmap to build a production‑grade AI microservice architecture.

AIJavaMCP

0 likes · 37 min read

Mastering Spring AI MCP: Bidirectional Communication, Four Providers, Sampling Callbacks, and Dual‑Mode Deployment

Ray's Galactic Tech

Apr 24, 2026 · Backend Development

From Bottlenecks to a High‑Concurrency Medical Assistant with LangChain4j

This guide details how to design and implement a production‑grade, high‑concurrency medical AI assistant using LangChain4j, Spring Boot, Redis, and Kubernetes, covering architecture, RAG‑enhanced retrieval, controlled tool invocation, guardrails, idempotent transactions, scaling strategies and observability to ensure reliable, compliant patient interactions.

Medical AIRAGSpring Boot

0 likes · 33 min read

From Bottlenecks to a High‑Concurrency Medical Assistant with LangChain4j

Ray's Galactic Tech

Apr 24, 2026 · Backend Development

Self‑Healing Agents: Rebuilding a High‑Concurrency Travel System with Spring AI ReAct

This article details how a legacy travel‑booking service was transformed into a production‑grade, self‑healing agent system using Spring AI ReAct and multi‑tool coordination, covering architectural redesign, tool governance, error semantics, high‑concurrency safeguards, observability, security, and real‑world performance gains.

AgentReActTool governance

0 likes · 31 min read

Self‑Healing Agents: Rebuilding a High‑Concurrency Travel System with Spring AI ReAct

Ray's Galactic Tech

Apr 23, 2026 · Artificial Intelligence

From Black‑Box to Explainable: Cloud‑Native AI Demand Engineering for Life‑Insurance

This guide explains why life‑insurance AI must move beyond black‑box recommendations, outlines eight production‑grade requirements, and presents a cloud‑native architecture that combines GraphRAG, rule engines, AI orchestration, observability, security, and Kubernetes to deliver explainable, auditable underwriting decisions.

Backend DevelopmentCloud Nativeartificial intelligence

0 likes · 37 min read

From Black‑Box to Explainable: Cloud‑Native AI Demand Engineering for Life‑Insurance