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paper/src/chapters/01-intro.tex
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@@ -14,14 +14,14 @@ This research effort touches a large variety of domains, spanning behavioral eco
\subsection{Motivation and Market Context} \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 most benchmarks and evals motivating \cite{Xia2025} the development of ability focusing on the performance of 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. Begging the question of how should these systems be designed for future robustness as well as how to maintain a competitive edge in the analytical components of these e-commerce platforms. \cite{MarkNtelAdvisors2025} 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 of the threat presented by the shift towards a growing share of the traffic coming from agents 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 or legal stakeholders responding to breaches and fraud, and the attackers/bot operators driving the automation.\cite{Imperva2025} 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 coming from large cases like Amazon against Perplexity \cite{AmazonvsPerplexity} which comes from the nature of the difficulty of identifying traffic coming from hybrid systems like the Commet browser, which is a system that this paper does explore in order to better understand the nature of how the interaction data looks like and what it means for dynamic pricing or recommendation systems down the line. 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 $N$. 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} \subsection{Solution Space Overview}
The dynamic pricing systems, as presented in \cite{Mueller2019}, often deal with sparse low-rank data of demand signals, which in combination with contamination from agents makes for a very complex set of interactions which have an impact on the pricing. To further complicate the problem in certain commercial settings, such as the one presented in \cite{Amjad2017}, we must address the true demand of products under censored observations. This sets us up with a good formulation for handling demand in our case of 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. 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 ($H$). An agent for purposes of our research is an algorithmic loop with the ability to access a web platform, perform actions on that platform such as (click, scroll and fill input field), the loop finishes in the moment when the judgement of provided definition of done by the internal large language model is satisfied. A detailed breakdown can be found in \cref{algagent-loop}. We formally define interaction data as coming from some actor which can either be an agent ($A$) or human ($H$). An agent for purposes of our research is an algorithmic loop with the ability to access a web platform, perform actions on that platform such as (click, scroll and fill input field), the loop finishes in the moment when the judgement of provided definition of done by the internal large language model is satisfied. A detailed breakdown can be found in \cref{algagent-loop}.