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. If you’re interested, you can check the full write-up here:
https://zflow.ai/zflow_ai_insights_article_3.html
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