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This tutorial walks Java backend developers through the mature Java LLM ecosystem, comparing frameworks like Spring AI and LangChain4j, and demonstrates step‑by‑step how to create a Spring Boot application with a chat endpoint, streaming responses, and dynamic model switching among OpenAI, Tongyi Qwen, and Ollama.
The final installment reviews the current AI agent ecosystem, forecasts emerging standards such as MCP and A2A, consolidates best‑practice guidelines for development, prompting, tool design, cost control and security, lists common pitfalls with debugging tips, and recaps the twelve‑episode series with a roadmap for further skill advancement.
This article walks through the design and implementation of a data‑analysis AI agent that converts natural‑language queries into SQL, executes them on a SQLite sales database, generates visualizations, and produces insight reports, complete with architecture diagrams and full Python code examples.
This tutorial walks through the functional planning, project layout, core Python code, context management, FastAPI service, and execution steps needed to create a full‑featured AI coding assistant that can generate, explain, refactor, fix, test, and review code.
After mastering core AI Agent capabilities, this article shows how to transform a prototype into a production‑grade service by covering a full architecture overview, stateless design, health‑check and graceful shutdown, asynchronous task queues, multi‑level caching, token‑cost optimization, model fallback, input/output filtering, rate limiting, monitoring, and deployment recommendations for different scales.
This article explains why AI agents behave like black boxes, introduces a three‑pillar observability framework (tracing, metrics, logging), demonstrates practical tracing with LangSmith and LangFuse, shows how to instrument agents with custom metrics, evaluate performance, and share best‑practice guidelines for production‑ready debugging.
This article explains why a single AI agent often falls short for complex tasks, outlines the benefits of multi‑agent collaboration, compares common architecture patterns, and provides hands‑on examples using AutoGen, CrewAI, and LangGraph, followed by a real‑world customer‑service team case and best‑practice guidelines.
This article explains the concept of Function Calling in large language models, walks through defining function schemas, shows step‑by‑step API call flows, demonstrates multi‑tool orchestration, parallel execution, tool‑chain composition, and integrates Function Calling with LangChain, while providing best‑practice guidelines and code examples.
This article explains why Retrieval‑Augmented Generation is needed, compares popular vector databases, provides step‑by‑step Docker and Python examples for Chroma, Pinecone, and Milvus, and shows how to optimize a full RAG agent with hybrid search, reranking, and caching.
This article walks Java developers through using Spring AI to build AI agents, comparing it with Python's LangChain, detailing architecture, environment setup, prompt templates, tool integration, RAG implementation, production‑grade features, and a side‑by‑side feature comparison.
