[Student IDEAS] by Alexandre Le Saux - Master in Data Sciences & Business Analytics at ESSEC Business School & CentraleSupélec
The article "AI Agent: Double-Edged Sword Between Convenience and Control" explores the integration of AI agents in daily life, highlighting their potential to revolutionize convenience while posing significant challenges to privacy and autonomy. As these agents become more embedded in our devices and systems, they offer remarkable efficiencies but also demand careful consideration of the ethical and practical implications. This piece invites readers to reflect on the balance between technological advancement and the preservation of human agency in an increasingly AI-driven world.
---
For several years now, we have been walking through the streets, often unaware that artificial intelligence (AI) agents embedded in various devices are observing us, analyzing our movements, and in some cases, making decisions that directly impact us without our knowledge or consent. We also lack any real understanding of the personal data being involved in these interactions. AI agents are autonomous systems that perceive their environment through various modalities (such as visual, textual, or audio inputs), process information using internal memory, databases, and models, and then act by retrieving information, generating content, or executing commands. In environments where these agents are increasingly embedded into daily technologies, we rarely realize how they might interpret or even use the information they gather about our environment. In some cases, such as with autonomous vehicles, we generally accept the agent’s decisions as necessary for safety and efficiency. However, newer AI-powered devices operate in the background without requiring transparency, raising major concerns about privacy, surveillance, and user control.
One of the most illustrative recent examples is the collaboration between Ray-Ban and Meta, which produced connected smart glasses. These are not simply high-tech gadgets; they are fashion-forward, discreet, and seamlessly integrated into daily life. What makes them significant is their embedded computer vision capabilities. Computer vision allows machines to interpret and understand visual data, simulating human vision to classify and process real-world environments. Combined with speech-to-text and text-to-speech capabilities, these glasses allow users to interact with their surroundings in new ways. Simple voice commands can activate environmental recognition programs that analyze the surroundings in real time. While such capabilities offer convenience and innovation, they also raise alarms about passive surveillance and the silent erosion of contextual consent and users nearby may not even know they are being recorded, and individuals wearing the glasses might not understand how all the data from their interactions are being stored, analyzed, and shared.
At first glance, AI-enhanced products, commonly referred to as AI agents, offer enormous potential. In appropriate contexts, they can provide real value. For example, they can greatly improve obstacle detection for visually impaired individuals, enhancing both personal and public safety. In healthcare, the transformative role of AI is well established, with systems complementing professionals by accelerating diagnosis and monitoring patients. The article Dr. ChatGPT by Karen Taubenberger offers an insightful overview of AI’s rapid expansion and role in modern medicine. Beyond clinical settings, these agents are being embedded into elder care robots, mental health applications, and even wearable biometric monitors, marking the beginning of ambient medical intelligence.
Connected tools in medicine illustrate both the advantages and the risks of AI agents. Traditional medical devices, like pacemakers, perform specific, predefined tasks. They do not share unnecessary data externally and are limited in scope. In contrast, AI-powered tools collect sensitive information, manage a wider range of functions, and automate decisions. Their general-purpose architecture is a fundamental shift from older models.
This architecture can be understood in layers: perception through multimodal inputs like audio or images; cognition through databases, memory, and language models; and action through decision-making, actuation, or API-driven operations. Bill Gates refers to AI agents as revolutionary technologies capable of centralizing and optimizing complex systems. Their appeal is clear: they can automate tedious routines and simplify tasks across multiple applications. Yet their layered structure also means that vulnerabilities at any level such as input spoofing, model bias, or decision-level hallucinations and ultimately it may result in compounding failures.
These agents are often described as "embodied cognition" in code: they perceive, process, and act. However, generalization tends to erode boundaries. Unlike specialized systems with clear limits, general-purpose AI agents blur distinctions between task categories and increasingly occupy gray zones of autonomy where legal and moral responsibility becomes harder to assign.
Despite their appeal, these tools demand a trade-off. AI agents access extensive personal data, applications, accounts, behavioral patterns. As we delegate more tasks, we also surrender a measure of control. This shift in agency isn’t just technological; it's psychological. Behavioral studies suggest that reliance on AI tools can reduce users’ engagement in critical thinking tasks, fostering a form of learned helplessness. For instance, Gerlich (2025) found that individuals who frequently use AI for decision support tend to offload cognitive effort, resulting in diminished critical thinking and increased passivity over time.
This raises an important question: are we gaining efficiency or losing autonomy? Our reliance on AI agents may gradually condition us to disengage from active decision-making. The tools built to assist can easily become tools that shape our behavior. As one reviewer puts it, the central issue is clear: efficiency at the expense of autonomy.
Warnings about over-automation are not new. As early as the 20th century, Georges Duhamel warned about technologies that might erode human agency. Today, AI agents like Retrieval-Augmented Generation (RAG) and large language models (LLMs) are increasingly embedded in our daily lives, making these concerns more urgent. These systems are trained on a vast corpora of online behavior and language, often reinforcing existing patterns or biases. They do not simply reflect knowledge but they also manufacture a context that may slowly replace our own judgment.
The evolution of smartphones illustrates this shift clearly. Originally designed for voice communication, they have evolved through stages that included SMS, cameras, apps, and now AI interaction. Since the launch of ChatGPT in 2022, text-based and conversational interfaces have accelerated. Earlier voice assistants such as Siri were often criticized for their limited abilities and privacy flaws. Modern models like Google Gemini show improvement, yet they are still confined to specific apps. They cannot yet handle multiple interconnected tasks from a single prompt such as booking a taxi, checking traffic, and confirming payment. The constraints are not only technical but regulatory. Executing cross-platform operations requires a unified interface layer and robust authentication models that are still under development.
Moreover, smartphones are no longer the neutral conduits they once were. Each application we first install and then use even only once is part of a broader platform economy, designed to collect, retain, and monetize user behavior. AI agents integrated into phones could eventually mediate all interactions, becoming the new gatekeepers between human intent and digital action. This adds a layer of power dynamics rarely acknowledged: the user no longer “uses” a device but interacts with a complex, semi-autonomous ecosystem governed by opaque commercial interests.
To address these limitations, companies have introduced new AI-first devices. Rabbit AI and Humane PIN AI aim to reinvent the smartphone experience. These devices reduce screen dependency and focus on voice-first commands. Humane PIN, for instance, uses a built-in projector to display information on the user’s palm. Attached to clothing, it acts as a quiet, always-available assistant. Devices like Rabbit AI and Humane PIN AI are examples of how companies are trying to redefine digital interaction and the future of communication.
These innovations challenge the longstanding screen-centric paradigm. Yet, they are also prototypes of a world where constant surveillance is embedded in fashion, mobility, and gesture-based computing. The implications go beyond convenience. These devices rely on persistent connectivity, continuous voice listening, and full access to cloud APIs. Their presence calls for a redefinition of "private space" not only spatially but cognitively. When every thought spoken aloud becomes a potential command, what becomes of internal deliberation?
Before embracing AI agents widely, we must address serious challenges.
First, giving AI agents full decision-making power introduces risks of misinterpretation. A simple command like "order a taxi" requires real-time understanding of location, preferences, app availability, and payment options. Early-stage agents may mismanage such tasks, causing frustration.
Second, consolidating data across services increases the risk of security breaches. Today, app data is compartmentalized; if one app is compromised, the impact is limited. AI agents integrate across platforms, which means a single vulnerability could expose everything. There is evidence that simple hacking methods, like SQL injection, have already been used to exploit such systems.
Third, AI hallucinations pose a threat. These errors, where the AI fabricates or misrepresents information, can turn convenience into risk. Misunderstood voice commands in critical situations such as banking could lead to serious consequences. Even more problematic is the issue of liability. If an AI agent misbooks a flight, who is responsible? The user, the developer, or the platform host?
Ethical issues abound as well. Agents can inherit algorithmic biases from their training data, reinforce stereotypes, or misinterpret cultural nuances. Without transparency about how decisions are made (known as explainability) users are left to accept outcomes without recourse. This becomes even more troubling when applied in legal, financial, or healthcare settings.
AI agents are increasingly embedded in business workflows. Tools like Zapier and N8N help automate processes, but they also require deep integration into private systems.
As public data becomes scarce, companies rely more on direct user data to train models often without clear consent. A recent example involves Mistral AI, which collected location data without informing users. This raises fundamental questions about corporate data ethics and user privacy (Usine digitale, 2024).
Moreover, AI agents are changing work itself. They are used in HR screening, performance evaluations, and productivity monitoring. While proponents claim they increase efficiency, critics warn they also increase surveillance, reduce employee autonomy, and entrench opaque decision-making. The risk is a “black box workplace” where workers no longer understand how or why decisions are made about their careers.
Popular culture often glorifies AI agents, as seen with Jarvis in Iron Man. But real-world systems lack that consistency. They are prone to errors, can be manipulated, and do not fully understand human context.
A safer path may be through specialization. Instead of one powerful agent managing everything, we can develop smaller agents with defined roles, each operating within secure limits. A central orchestrator could coordinate these agents, reducing risk while preserving functionality.
As AI enters fields like genomics and nanotechnology, guided governance will be crucial. Technological power must be balanced with responsibility. Only by embedding transparency, segmentation, and user control into AI agent development can we ensure they serve us rather than control us. This includes legislative efforts such as the European Union’s AI Act, which calls for risk-tiered regulation and prohibits certain forms of manipulative AI. While no law can future-proof society against all technological disruption, forward-thinking policy must shape the terms of deployment not react to its consequences.
[1] Taubenberger, K. (2024). Dr. ChatGPT: The Promise and Peril of Generative AI in Healthcare. The New Atlantis.
[2] Gates, B. (2023). The Age of AI has begun. GatesNotes. https://www.gatesnotes.com/ai-agents
[3] Duhamel, G. (1930s, cited conceptually). Known for his early 20th-century warnings about the dehumanizing effects of mechanization and automation.
[4] Usine Digitale. (2024).L'IA de Mistral traçait les utilisateurs à leur insu dans la première version du chatbot.