Designing Self-Building AI Agents with LangChain4j: Supervisor vs. Workflow Patterns
This article explores an experiment where an LLM-backed code assistant used LangChain4j to design and implement a multi-agent coding system capable of writing, testing, and debugging code. It compares two agentic AI patterns, Supervisor and Workflow, highlighting their performance trade-offs and the importance of monitoring in AI-driven systems. The experiment provides insights into orchestrating LLMs for complex, autonomous tasks.
Read original on InfoQ ArchitectureThe article details a fascinating meta-experiment where a code assistant, powered by a Large Language Model (LLM), was tasked with building a version of itself using the LangChain4j framework. The goal was to create a multi-agent system that could autonomously write, test, and debug code, much like a human engineer.
Agentic System Architecture: Supervisor Pattern
Initially, the code assistant chose LangChain4j's Supervisor pattern for its design. This pattern involves a central supervisor agent that orchestrates specialized sub-agents. The resulting architecture comprised four key sub-agents:
- ExplorerAgent: Explores the codebase.
- PlannerAgent: Creates a plan for implementation.
- ImplementerAgent: Writes or edits code based on the plan.
- ExecutorAgent: Compiles and executes the generated code and tests.
Each sub-agent was equipped with necessary tools, such as a file system explorer, code editor, and a code runner. This hierarchical orchestration mimics how a human engineer or professional code assistant might approach a complex task.
Testing and Model Dependencies
When testing the self-built agent, an interesting observation was made regarding LLM model capabilities. An older, cheaper model (gpt-4o) failed due to entering a "tool-calling loop," indicating it struggled with complex orchestration and decision-making within the agentic system. In contrast, a newer, more advanced model (gpt-5-mini) successfully fixed the buggy code and passed all tests. This highlights the critical dependency of agentic system performance on the underlying LLM's reasoning and tool-use capabilities.
Supervisor vs. Workflow Patterns
The experiment compared the Supervisor pattern with a Workflow-based approach. The Supervisor pattern, while offering high autonomy, introduced significant LLM-induced coordination overhead, leading to slower execution. The more rigid, deterministic Workflow pattern executed approximately three times faster by explicitly defining a sequence of actions and review loops, minimizing the need for constant LLM-driven decision-making for task flow management.
Architectural Choice: Autonomy vs. Efficiency
When designing AI agent systems, there's a clear trade-off between the flexibility and autonomy offered by a supervisor-driven approach and the improved efficiency and predictability of a workflow-based model. For tasks requiring dynamic problem-solving, a supervisor might be suitable, but for repetitive or well-defined processes, a workflow can offer significant performance gains by reducing LLM inference calls and coordination overhead.
Monitoring Agentic Systems
The article also touches upon the importance of observability in complex agentic systems. LangChain4j's `MonitoredAgent` interface allows for tracing agent invocations and visualizing the system topology, providing crucial insights into how AI agents operate and interact. This monitoring is essential for debugging, performance optimization, and understanding the decision-making process within these autonomous systems.