## Data Input

Here, all the loaded datasets are synced into a SQL connection. It's necessary to be compatible with the SQL tool. We grouped datasets by theme so that each agent has only a limited number to query.

![Data Input.png](EH9JpM2lXh5e)

## Markdown Model

To evaluate the impact of pricing on demand, we use a demand forecasting model trained specifically for this project. This model will then be queried by the markdown agent.

![Markdown Model.png](UwxksLkSctYg)

## Agent

All the datasets are connected to the [Store Assistant](ai_agent:XoFPzwEm) through the [Semantic Model Tool](agent_tool:oIam6hp). The markdown agent is also connected to the Store assistant to enable the evaluation of the price impact.

The Agent will then connect to the [Dynamic Selling Assistant](web_app:ud5isHW) via Agent Hub.

![Agents Connect.png](8TXsEjWw8EfW)


## Prebuilt questions

You don't have to ask all the questions through the Agent Connect interface. If you have repeated questions, you can add them to the [questions](dataset:questions) dataset and use a time trigger to build the responses.

![Prebuilt Question.png](Ex4XEMKABIJl)


