#spring ai
17 articles tagged spring ai, from AI, AI Engineering, Java. Latest: “I Rebuilt Our AI Agents on Kafka Instead of REST — Here's the Full Java Architecture”.
I Rebuilt Our AI Agents on Kafka Instead of REST — Here's the Full Java Architecture
REST between AI agents works until agent #4 shows up. The full Java/Spring Kafka topic design, the ReAct loop as a state machine, and a free audit trail.
Designing the AI Request Pipeline: 8 Layers Between User Input and Your LLM
A production architecture blueprint for every layer between a user's message and a safe, reliable LLM response — with Spring Boot code you can actually use.
Building AI Skills: GitHub Copilot Extensions, Claude Tools, and Reusable Agent Capabilities
One skill, three platforms. Building a custom capability for GitHub Copilot, Claude, and your own AI agents — with real code and a decision framework.
Java Developer Roadmap 2026: What to Learn, What to Skip
An opinionated roadmap for Java developers in 2026 — Java 21-24, Spring Boot 3, virtual threads, Spring AI, cloud-native patterns, and what to skip.
Event-Driven AI: How Kafka Streams Changed the Way We Run LLMs in Production
From blocking HTTP calls to async event streams — the architecture shift that halved latency, eliminated 3AM pages, and lets us process 2M AI requests a day.
Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists
Your LLM forgets everything once the request ends. How production systems build short-term, long-term, and semantic memory — with Spring AI and pgvector.
Spring Boot Complete Architecture Guide: From Foundation to Spring AI
An end-to-end guide to Spring Boot architecture for experienced developers — IoC, AOP, security, data, resilience, testing, Spring AI, and deployment.
Spring AI vs LangChain4j: Which Should You Choose?
A balanced comparison of Spring AI and LangChain4j for building LLM apps in Java — design philosophy, provider support, RAG, tools, and when to pick each.
How to Stream LLM Responses in Spring AI (Server-Sent Events)
Stream tokens as they're generated with Spring AI's ChatClient — SSE with WebFlux and Spring MVC, and handling errors in production.
Building a RAG Chatbot with Spring AI and PGVector
A step-by-step guide to a production-ready RAG chatbot in Java with Spring AI, PostgreSQL, and pgvector — ingestion, embeddings, retrieval, grounded answers.
What's New in Modern Java — and How to Build AI With It
Modern Java (21→25) brings virtual threads, structured concurrency, records, and the FFM API. Here's what's new and how it powers AI apps.
Spring AI: From Beginner to Expert, Course Overview
A 7-lesson Spring AI course for Java teams: setup, prompting, tool calling, RAG, MCP, choosing a model and running AI features in production.
Tool Calling with Spring AI
A hands-on guide to Spring AI tool calling — define @Tool methods, wire them into ChatClient, and let Claude safely call your Java services.
Building an MCP Server with Spring AI
Most MCP content is Python/TypeScript. Here's how to build an MCP server in Java with Spring AI — expose your services as tools any MCP client can use.
Structured Outputs with Claude: Reliable JSON Every Time
Parsing JSON out of prose is a production liability. A practical guide to reliable, schema-conformant structured output from Claude, with Spring AI examples.
Prompt Caching: Cut Your LLM Costs by 80%
A practical guide to Anthropic and OpenAI prompt caching — how it works and how to implement it in Spring AI to cut latency and API costs.
Spring AI Enterprise Integration Guide
A practical guide to building enterprise-grade AI applications with Spring AI — from chat clients and RAG pipelines to production observability.