Control agent turns, not models, to cut LLM costs

A common issue in multi-agent AI systems, leading to excessive API costs and broken workflows, is the tendency for agents to enter conversational loops or excessively hand off tasks. The author argues that instead of switching between models like Claude and GPT, the primary solution lies in controlling the number of turns an agent takes. Frameworks like OpenAI Agents SDK and LangGraph offer built-in mechanisms to limit these turns, suggesting that the problem is often one of control and graph logic rather than model intelligence. AI

IMPACT Focusing on controlling agent turns can significantly reduce operational costs and improve the reliability of AI agent systems.

RANK_REASON The item is an opinion piece discussing best practices for managing LLM agent workflows and costs, rather than a direct release or announcement.

Read on dev.to — LLM tag →

• Claude
• Claude Opus-4.6
• generative pre-trained transformer
• GPT-5.4
• langgraph
• Make
• n8n
• OpenAI Agents SDK
• OpenClaw
• r/openclaw
• Zapier

• product
• infra

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