LangGraph agent designs show no significant accuracy difference despite cost variations

A comparative study evaluated four different LangGraph agent designs (ReAct, plan-and-execute, supervisor, and draft-verify) using a fixed set of 240 synthetic questions about Japanese statutes and a local Qwen3.5:9b model. The study found that while cost varied significantly between designs, with plan-and-execute being the most expensive, there was no statistically significant difference in accuracy among them. The draft-verify design showed a slight advantage over plain ReAct in terms of correctness, but overall, the more complex designs did not yield measurably better results for the specific task. AI

IMPACT This research suggests that simpler agent architectures may be sufficient for certain tasks, potentially reducing computational costs without sacrificing accuracy.

RANK_REASON The item details a comparative study of different AI agent designs and their performance on a specific task, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• draft-verify
• e-Gov Law API
• Japanese statutes
• LangGraph
• Plan and Execute a Successful Virtual FACT Inspection with Zero Citations
• Qwen3.5:9b
• ReAct
• supervisor

• other

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