This is a cost-benefit problem (or equivalently, a profit-optimisation problem) that is not fully answered by the regression model. This kind of problem requires you to combine your statistical inference about the product sales with economic analysis. Generally this entails writing the profit function for the firm as a function of the number of sales representatives, by including the sales and the variable costs of employing those representatives. You use your regression model to estimate the "sales function" for the firm and you then get an estimate of the "profit function" for the firm. You can then estimate the optimal number of sales representatives by taking account of the estimated number of sales you will get and the known variable costs (e.g., salary, etc.) of employing them.
A secondary problem here is whether you can interpret your regression outcomes causally, such that if the firm changes the number of sales representatives, they will thereby change the number of product sales. Using passive observation, it is unlikely that your regression outputs represent purely causal effects, and so you may need to do some further work later to deal with this. This is complicated, so I will just leave it as a caveat, and in the analysis below I will assume you can interpret your regression output in a causal manner.
Setting up a profit-optimisation problem: Let $r$ denote the number of sales representatives that are employed by the firm and let $c$ denote the "variable costs" of employing an additional sales representative (e.g., salary costs, etc.). Moreover, let $\mathcal{P}$ be the set of products sold by the firm and let $\pi_i$ be the profit from the sale of a single item of that product (i.e., the revenue for that item less variable costs of getting that item) for each product $i \in \mathscr{P}$. Let $S(i, r)$ be the number of sales of produce $i$ when the firm has $r$ sales representatives (and note that this part is stochastic --- hence the upper case). Then the profit function for the firm is:
$$\pi(r) = \sum_{i \in \mathcal{P}} \pi_i \cdot S(i,r) - r \cdot c - \text{Other Fixed Costs}.$$
In econometric analysis for input factor optimisation by the firm, we generally wish to maximise the expected profit $\mathbb{E} (\pi(r))$ (which assumes that shareholders are risk-neutral at the margins). Your regression model is giving you a model for the stochastic part $S$, which lets you estimate this part, giving you some estimator $\hat{S}$ for the expected sales. Assuming you can estimate over all the products, you then have an estimator for the expected profit, which is:
$$\hat{\pi}(r) = \sum_{i \in \mathcal{P}} \pi_i \cdot \hat{S}(i,r) - r \cdot c - \text{Other Fixed Costs}.$$
The estimated optimal number of sales representatives is:
$$\hat{r} = \underset{r \in \mathbb{N}}{\text{arg max}} \ \hat{\pi}(r).$$
Assuming some basic properties that ensure smoothness and quasi-concavity of the revenue function, the optimum (real) number of sales representatives will occur at the "critical point" where the marginal revenue of an additional sales-rep is equal to the marginal cost (i.e., salary, etc.) --- i.e., we have:
$$\text{Marginal revenue}(\hat{r}) \equiv \sum_{i \in \mathcal{P}} \pi_i \cdot \frac{\partial \hat{S}}{\partial r} (i,\hat{r}) = c = \text{Marginal Cost}.$$
This is a standard result in the economics of firm behaviour --- under some basic regularity conditions, profit maximising firms will employ factors of production up to the point where their marginal revenue equals their marginal cost (called the profit maximisation rule).
This gives you a simple outline of how you might pose an econometric optimisation problem of this kind. As you can see, the answer here is affected not only by the inferences from the regression model, but also by the variable costs of employing the sales representatives. If the salary costs of employment increase then, ceteris paribus, we would expect the optimal number of sales reps to decrease, and vice versa.
