%% Cite like this \parencite{knuth1984texbook}. See \cref{fig:example}.
%% \begin{figure}[h]
%%   \centering
%%   \includegraphics[width=.7\linewidth]{figures/example.pdf}
%%   \caption{Example figure.}
%%   \label{fig:example}
%% \end{figure}

\section{Introduction}

In this paper we present an exploration and defense against the presence of new commercial entities in digitally powered platforms. This research establishes the following contributions: definition and formalization of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors across a large variety of digital systems, construction of a discriminative model (to prove separability) as a strong learner for downstream mitigation of contamination by non-human entities, translation of such learned separability into existing dynamic pricing machine learning loops, and finally establishment of a high-level KPI-affecting causal effect and cost-saving framework for the future of internet commerce in the presence of such non-human learners.

This research effort touches a large variety of domains, spanning behavioral economics for understanding the rationality of behavior as theorized by the concept of homo economicus, agent-based modeling to translate our learned separability into disjoint dynamic pricing systems, reinforcement learning which serves as the SOTA for price-learners, and dynamic pricing and market equilibrium theory to understand the risks of possible supra-competitive pricing phenomena in cases of adversarial pricing systems driving the market out of equilibrium.

\subsection{Motivation and Market Context}

The current innovation boom in generative artificial intelligence and its applications to knowledge-based work tasks has brought many competing technologies for browser-use automation, with benchmarks and evaluations \cite{Xia2025} motivating the development of capabilities focused on commercial research, understanding, and transaction execution \cite{Xie}. The ``AI Agent'' market is forecasted to grow from around USD 5-8 billion in 2025 to USD 42-52 billion by 2030. This surge reflects adoption in e-commerce, customer service, and enterprise automation, where agents handle interactions previously done by humans, raising the question of how these systems should be designed for future robustness as well as how to maintain a competitive edge in the analytical components of e-commerce platforms \cite{MarkNtelAdvisors2025}.

The key stakeholders affected by the threat of increasing agent-driven traffic include online businesses and platform operators (especially in bot-heavy sectors like retail, travel, and financial services), their security, fraud, and engineering teams, end users whose accounts and data are exposed and whose experience degrades, regulators and legal stakeholders responding to breaches and fraud, and the attackers or bot operators driving the automation \cite{Imperva2025}.

The industry has already seen legal action in cases like Amazon against Perplexity \cite{AmazonvsPerplexity}, stemming from the difficulty of identifying traffic from hybrid systems like the Commet browser. This paper explores such systems to better understand what the interaction data looks like and what it means for dynamic pricing and recommendation systems downstream. This observed impact indicates a need for prevention of secondary negative effects on the ``legacy'' systems which power modern revenue sources for many companies. Dynamic pricing algorithms rely on directly translating demand features $q$ to new price assignments $\hat{p}$ across a catalogue of products of size $N$.

\subsection{Solution Space Overview}
Dynamic pricing systems, as presented in \cite{Mueller2019}, often deal with sparse low-rank data of demand signals which, combined with contamination from agents, creates complex interactions that impact pricing. To further complicate the problem, certain commercial settings such as the one presented in \cite{Amjad2017} must address the true demand of products under censored observations. This provides a formulation for handling demand in our case with multiple kinds of commercial mediators: $\hat{q} \gets q_A + q_H$ where $q_A$ represents the distribution of demand generated by agentic mediators and $q_H$ represents that of true human demand.

We formally define interaction data as coming from some actor which can either be an agent ($A$) or human ($H$). For purposes of this research, an agent is an algorithmic loop with the ability to access a web platform and perform actions such as clicks, scrolls, and input field fills. The loop terminates when the internal large language model judges the provided task definition as complete. A detailed breakdown can be found in \cref{algagent-loop}.


\begin{algorithm}[t]
\DontPrintSemicolon

\SetKwInOut{Input}{Input}
\SetKwInOut{Output}{Output}

\Input{Goal $G$, Platform URL $u$, LLM $\mathcal{M}$}
\Output{Task completion result $r$}

Initialize browser instance $\mathcal{B}$ with connection to $u$\;
Construct prompt $\pi \gets \textsc{BuildPrompt}(G, u)$\;
$\text{done} \gets \text{False}$\;

\While{$\neg \text{done}$}{
    Observe current page state $s_t$ from $\mathcal{B}$\;
    Query $\mathcal{M}$ with $(\pi, s_t)$ to determine next action $a_t \in \{\text{click}, \text{scroll}, \text{fill}, \text{navigate}\}$\;
    Execute $a_t$ on $\mathcal{B}$ to transition to state $s_{t+1}$\;
    $\text{done} \gets \mathcal{M}.\textsc{JudgeCompletion}(G, s_{t+1})$\;
}

Extract final result $r$ from terminal state\;
\Return{$r$}\;

\caption{AI Agent's Interaction Loop}
\label{algagent-loop}
\end{algorithm}


The previously described goal of separability allows us to formulate a task which entails taking raw interaction data for either actor and creating a composite demand estimate $\hat{q}$.
