This article details the construction of a multi-agent hiring workflow using LangChain4j and LangGraph4j on the Spring Boot framework. The workflow involves several AI agents independently evaluating candidate resumes, with a graph aggregating their scores to make a routing decision. The system is designed to involve human review when the AI's signals are unclear, and it can operate with either mock or real LLM backends, including local CPU-only Ollama models. The author highlights the benefits of LangGraph4j for managing concurrent agents, human-in-the-loop processes, and durable state tracking, as well as the ease of use provided by LangChain4j's AiServices for defining agent interfaces and integrating tools. AI
IMPACT Demonstrates practical application of multi-agent systems for complex business processes like hiring.
RANK_REASON The article describes the implementation of an AI-powered workflow using existing frameworks, rather than a new model release or core research.
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