Stop Burning Money on AI Agent Tokens: A Practical Cost Optimization Guide

AI agents are powerful — but they can drain your API budget faster than you can say "context window." If you're running autonomous agents on OpenClaw or similar platforms, you've probably seen those API bills climb. Here's a practical, no-fluff guide to cutting your agent's token costs by 40-70% without sacrificing capability. The Hidden Cost Problem Most AI agent frameworks treat every interaction the same way: stuff the entire context into the prompt, send it to the most capable model, and hope for the best. This works great for demos. It's terrible for production. Here's what actually eats your budget: Bloated system prompts loaded on every single call Full conversation history replayed for simple tasks Premium models used for trivial operations (GPT-4 for string formatting, really?) Retry storms when prompts are poorly structured Redundant tool calls because the agent forgot what it already retrieved Let's fix each one. 1. Tiered Model Routing The single biggest cost saver. Not eve