ai
LangChain4j Agentic Workflows: From AI Calls to Multi-Agent Systems in Java
Learn how to orchestrate multiple AI agents in Java using LangChain4j's agentic module, from sequential pipelines and loops to goal-oriented planning and LLM-driven supervisors.
2026, Mar 01 — 24 minute(s) readFrom Lonely Agents to Talking Teams: An introduction to A2A
This article introduces A2A, a protocol that lets AI agents stop working in isolation and start collaborating as real “teams.” You’ll learn how agents can discover each other via agent cards, exchange tasks and multimodal artifacts, handle errors and auth, and how A2A compares to MCP. With a practical Java + LangChain4j + Quarkus example (superhero-themed, of course), it shows how to build both an A2A server and client so your agents can coordinate, delegate, and actually work together.
2025, Nov 28 — 19 minute(s) readAI-Powered Form Wizards: Chat, Click, Done
Discover how AI-powered form wizards turn confusing forms into smooth, conversational experiences. This article walks you through building an intelligent assistant with Quarkus and LangChain4j—able to guide users, validate data, retrieve missing information with RAG, and even process uploaded documents. From structured output to multimodal inputs, learn how to transform traditional forms into smart, adaptive, user-friendly interactions.
2025, Apr 15 — 15 minute(s) read