Developer Builds Multi-Agent AI Hiring Workflow Using LangChain4j and Spring Boot

A software developer built a multi-agent hiring workflow using LangChain4j, LangGraph4j, and Spring Boot to move beyond basic AI demos and explore real-world agentic design. The system uses four concurrent AI agents to independently score job candidates across skills, experience, cultural fit, and red flags, then aggregates results to route decisions automatically or escalate to human review. LangGraph4j's StateGraph was chosen to enable parallel agent execution, durable state via a Postgres checkpointer, and resumable human-in-the-loop pause points that can persist across restarts. Each agent is defined as a plain Java interface, with LangChain4j handling model calls, JSON parsing, and tool invocation without manual boilerplate. The developer also tested the workflow against a local CPU-only Ollama model, drawing practical lessons about running agentic pipelines outside controlled demo environments.