The Four Axes of AI Agent Efficiency: When to Use LLMs (And When Not To)

What You Ask the Model to Do Matters More Than Which Model You Use Most advice about AI agent costs starts and ends with tokens. Cache your prompts. Batch your requests. Use a cheaper model. And those tactics help, the same way compressing images helps a slow website. They’re optimizations at the wrong layer. The bigger problem is architectural. Teams building multi-agent systems default to routing everything through an LLM because it’s the easiest pattern, not because it’s the right one. Every status check, every file validation, every data comparison, every formatted notification goes through a model that charges per token and introduces the possibility of hallucination on every call. The convenience of “just let the AI figure it out” becomes a tax on every operation in the system. Gartner predicts that over 40% of agentic AI projects will be canceled by 2027 due to escalating costs and unclear value. The escalating costs are addressable. The unclear value problem is a different chal