For each simulated week, the agent can take actions for unlimited turns across 34 tools in the categories displayed in the table below. These categories cover pricing and plan design, growth and market expansion, product quality and research, reliability and support, information acquisition, public communication, and enterprise sales. Each tool accepts fine-grained structured arguments, so agents can compose a large space of possible policies.

An agent makes profits through customer subscription payments and in-product ad monetization. We abstract the company product that customers subscribe to as a numerical product quality. Higher product quality results in more product subscriptions and payments, but maintaining quality via development, research, infrastructure capacity, support, and model tier choices requires spending. Acquiring customers through advertising channels also costs money. Cash therefore changes through both immediate costs and delayed revenue effects.

\[ \begin{aligned} \underbrace{B_{t+1}-B_t}_{\substack{\text{daily cash}\\\text{change}}} ={}& \underbrace{Y_t^{\mathrm{sub}}}_{\substack{\text{subscription}\\\text{payments}}} +\underbrace{\sum_iY_{i,t}^{\mathrm{ads}}}_{\substack{\text{in-product}\\\text{ads}}} -\underbrace{K_{\kappa_t}^{\mathrm{capacity}}}_{\substack{\text{capacity}\\\text{cost}}} -\underbrace{\sum_p \chi_p^{\mathrm{usage}}U_{p,t}^{\mathrm{use}}}_{\substack{\text{usage compute}\\\text{cost}}}\\ &-\underbrace{x_t^{\mathrm{ops}}}_{\substack{\text{support}\\\text{spending}}} -\underbrace{x_t^{\mathrm{dev}}}_{\substack{\text{dev}\\\text{spending}}} -\underbrace{X_t^{\mathrm{target\text{-}ops}}}_{\substack{\text{targeted}\\\text{support}}} -\underbrace{\sum_gx_{g,t}^{\mathrm{target\text{-}dev}}}_{\substack{\text{targeted}\\\text{dev}}}\\ &-\underbrace{\sum_{c,g}x_{c,g,t}^{\mathrm{ads}}}_{\substack{\text{acquisition}\\\text{ads}}} -\underbrace{N_t^{\mathrm{lead}}c^{\mathrm{lead}}}_{\substack{\text{lead}\\\text{acquisition}}} -\underbrace{K_t^{\mathrm{market}}}_{\substack{\text{market}\\\text{research}}} -\underbrace{K_t^{\mathrm{group}}}_{\substack{\text{group}\\\text{research}}} -\underbrace{K_t^{\mathrm{project}}}_{\substack{\text{research}\\\text{projects}}} \end{aligned} \]

Agents acquire new customers by spending on advertising channels. Each customer group reacts differently to each ad channel, so the same spend can produce different acquisition rates across groups. Reputation, social media reactions, market saturation, demand surges, and macroeconomic conditions also affect acquisition speed. We sample daily new prospects from a Poisson distribution parameterized by this expectation. Market research can reveal additional customer groups and improve what the agent knows about known groups. Enterprise customers follow the same price and quality logic, but deals are negotiated through offers, counter-offers, reply delays, and possible rejection.

\[ \begin{aligned} \underbrace{\mathbb{E}\!\left[n_{g,t}^{\mathrm{prospect}}\right]}_{\substack{\text{expected new prospective}\\\text{customers for group }g}} ={}& \underbrace{R_{g,t}}_{\substack{\text{reputation}\\\text{in group }g}} \cdot \underbrace{D_{g,t}}_{\substack{\text{market saturation}\\\text{for group }g}} \cdot \underbrace{C_t}_{\substack{\text{calendar}\\\text{cycle}}} \cdot \underbrace{M_{g,t}}_{\substack{\text{macro econ}\\\text{cycle}}} \cdot \underbrace{A_{g,t}}_{\substack{\text{social media}\\\text{reaction}}} \cdot \underbrace{Z_t}_{\substack{\text{demand}\\\text{surge}}}\\ &\cdot\left( \underbrace{\sum_c\frac{x_{c,g,t}L_{c,g,t}}{x_{\mathrm{ad}}}}_{\substack{\text{leads from each}\\\text{ad channel}}} +\underbrace{\sum_hN_{h,t}W^{\mathrm{net}}_{h,g}}_{\substack{\text{networking effect}\\\text{from each group}}} \right) \end{aligned} \]

Product quality is affected by daily development, research projects, model tier choices, targeted development, infrastructure capacity, support spending, usage quotas, and in-app ad strength. These controls shape customer experience through base product quality, quota saturation, system outages, support delays, relationship history, and ad load. Competitors add pressure by periodically raising customer quality expectations. Broad product development and research can make competitors catch up faster, while targeted development for specific groups is harder to copy and lets competitors catch up more slowly.

\[ \begin{aligned} \underbrace{Q_{i,t}^{\mathrm{perc}}}_{\substack{\text{quality perceived}\\\text{by customer }i}} ={}& \underbrace{m_p}_{\text{model-tier effect}}\left( \underbrace{q_0}_{\text{initial quality}} +\underbrace{b_t^{\mathrm{shared}}}_{\text{dev improvement}} +\underbrace{b_{g,t}^{\mathrm{group}}}_{\text{targeted dev improvement}} \right) -\underbrace{\beta_o o_t}_{\text{overload penalty}} -\underbrace{\beta_{\mathrm{out}}\mathbb{1}\{\mathrm{outage}_t\}}_{\text{outage penalty}}\\ &+\underbrace{\beta_r(r_{i,t}-r_0)}_{\text{customer relationship}} +\underbrace{\beta_d\log\!\left(\alpha_d+d_{i,t}/d_0\right)}_{\text{customer stickiness}} -\underbrace{\beta_I I_{i,t}}_{\text{open issues penalty}} -\underbrace{\beta_U\left(\nu_U-\frac{U_{p,t}}{D_Uu_i}\right)_+}_{\text{quota saturation penalty}} -\underbrace{\eta_i^{\mathrm{ads}}a_{i,t}^{\mathrm{eff}}}_{\text{in-app ads penalty}} \end{aligned} \]

Many actions have delayed and uncertain effects, forcing long-horizon decision making under uncertainty. Costs may appear immediately, while the corresponding revenue, retention, research, or reputation effects arrive weeks later. R&D projects have stochastic completion timelines and quality improvements, so investing more does not deterministically produce an immediate gain. Enterprise negotiations also unfold over stochastic delays, making it costly to wait too long but risky to overreact to any single turn.