Improve agent performance through deliberate iteration.
Use monitoring data to make targeted improvements to your agents.
Improving anything other than the bottleneck is wasted effort.
Consistent 1% improvements produce transformative results over time.
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
The optimal amount of optimization is not infinite — there is a point where you should stop and move on.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Change one thing at a time so you can attribute improvements to specific changes.
Optimization improves within a framework; innovation replaces the framework. Know which you need.
Making an agent faster means it can serve you more often with less friction.
An agent that acts fast but wrong is worse than one that acts slowly but right.
A reliable agent works every time, not just when conditions are perfect.
An agent that tries to do too much does nothing well. Optimize by narrowing scope to what matters.
An efficient agent achieves results with minimal energy expenditure — cognitive, emotional, or physical.
Optimize how agents connect and hand off to each other, not just how each agent performs in isolation.
The most powerful optimization is often subtraction — removing steps that add cost without adding value.
Dedicate focused time blocks to optimizing specific agents rather than trying to optimize everything continuously.
Without a baseline measurement, you cannot know whether your optimization actually improved anything.
Record what you changed, why, and what happened — optimization without documentation is gambling.
Optimizing before you understand the system is the root of much wasted effort.
Optimization is not something you do once — it is an ongoing relationship with your systems.