> For the complete documentation index, see [llms.txt](https://docs.amesa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amesa.com/build-multi-agent-systems/create-a-project.md).

# Create a Use Case

A use case is a collection of agent orchestrations for the same problem. The best way to use AMESA is to build multiple orchestrations within the same use case using different design patterns and variations. This allows you to iterate and improve for the best possible performance.

All orchestrations within a use case share the same simulator and the same overall KPI.

## Create a Use Case in the UI

To create a use case, click on New Use Case in the upper right-hand corner of your dashboard.

<figure><img src="/files/PXTYvNiTNG1SCfrHFxrt" alt="" width="375"><figcaption></figcaption></figure>

You'll be prompted to enter the simulator associated with your use case and then choose your team.

### Set KPI and ROI

After you select your simulator and team, you'll be prompted to set the KPI and ROI for your use case.

<figure><img src="/files/0EEI3rsanS9V22evP6Sz" alt=""><figcaption></figcaption></figure>

The KPI is the top-level goal, the most important metric you are trying to optimize that you will use to evaluate the performance of your team of agents. Choose the sensor variable that represents this metric to serve as your KPI.

For benchmark value, enter the current performance level on the KPI. This is the number that your agents will attempt to outperform. Add the benchmark units where prompted.

The ROI section allows you to represent agent performance in terms of financial impact. Enter the value in dollars of a specific percentage improvement in the KPI (either increase or decrease depending on the problem).

After agents are trained, the platform will use this information to provide data and visualizations about the performance on the KPI compared to the benchmark and about the financial return on investment on the Benchmarks page.


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# Agent Instructions
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