Token Cost Optimization for AI Agents: 7 Patterns That Cut Our Bill by 73%

Token Cost Optimization for AI Agents: 7 Patterns That Cut Our Bill by 73% Six months ago our monthly LLM bill at RapidClaw hit a number I'd rather not print. We were running production AI agents across customer workloads, and every "let's just add one more tool call" was quietly compounding into a four-figure surprise on the invoice. I'm Tijo Bear, founder of RapidClaw. We build infrastructure for teams who want to ship AI agents without becoming full-time prompt engineers. After spending a quarter obsessing over our own token economics, we cut spend by 73% — without degrading agent quality. Here are the seven patterns that mattered most. 1. Prompt caching is the cheapest 90% win you'll ever ship If you're sending the same system prompt, tool definitions, or RAG context on every turn, you're paying full freight for tokens the model has already seen. Anthropic, OpenAI, and most major providers now support prompt caching with cache hits priced at roughly 10% of normal input tokens. # Be