HOW I CUT AN AI AGENT'S TOKEN USE BY 94%

Summary from the TLDR Dev newsletter

A specialized AI agent workflow was optimized by transitioning from a natural language instruction model to a compiled code version, which reduced token usage by 94% and latency by 87%. The initial natural-language skill helped the discovery of a stable workflow, allowing for the identification of components that could be transformed into deterministic code, with LLM calls reserved for the parts requiring language understanding.