Article: The Self-Building Agent: A LangChain4j Experiment
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Experiment with self-building agent using LangChain4j, highly relevant to AI agent development.
A meta-experiment using LangChain4j directed an LLM-backed code assistant to design and implement a multi-agent coding system from its own documentation. The resulting self-built agent, using a supervisor pattern with four sub-agents (Explorer, Planner, Implementer, Executor), successfully fixed real bugs and passed tests. The experiment revealed that a rigid workflow pattern executes three times faster than the autonomous supervisor pattern by eliminating LLM-induced coordination overhead, and that newer models avoided tool-calling loops that plagued older, cheaper models.