Agent Cost and Latency Optimiser
Analyses an agent's design and returns a specific plan to cut cost and latency — what to cache, what to shorten, where a smaller model works, with the estimated saving for each.
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Running it, start to finish
- Measure where tokens actually go before changing anything.
- Enable caching and shorten the system prompt first.
- Verify quality has not dropped after each change.
What you get back
The output this produces, every time.
- Breaks down where tokens go, showing that the system prompt is paid on every call.
- Identifies what can be cached, which is frequently the largest saving with no behaviour change.
- Names what not to optimise, since a cheaper agent that fails more often costs more.
Getting better results
Where this usually goes wrong, and how to avoid it.
- Look at the system prompt first. It is paid on every request and is the last thing anyone examines. Shortening it compounds across all volume.
- Enable prompt caching before anything else. Where the provider supports it, it is usually the biggest single saving and it changes no behaviour.
- Be careful with model routing. A smaller model failing on a task it cannot handle costs more in retries and errors than the routing saved.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.