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