Stop tuning LLM agents with live API calls: A simulation-based approach

LLM agent configuration is a surprisingly large search space, including model choice, thinking depth, timeout, and context window. Most teams pick a setup once and never revisit it. Manual tuning with live API calls is slow and expensive, and usually only happens after something breaks. We explored a different approach: simulate first, then deploy. Instead of calling the model for every trial, we built a lightweight parametric simulator and replayed hundreds of configuration variants offline. A scoring function selects the lowest-cost configuration that still meets quality requirements. The full search completes in under 5 seconds. A few patterns stood out: Many agents are over-configured by default Token usage can often be reduced without impacting output quality Offline search is significantly faster than live experimentation In practice, this approach reduced token cost by around 20-40% on real workloads. We’re currently preparing the open-source release of the OpenClaw Auto-Tuner.