As the initial excitement surrounding generative artificial intelligence (AI) has subsided, a critical question has emerged: how can these technologies create tangible benefits in our daily lives? This inquiry is particularly relevant considering that AI chatbots, despite their versatility in generating text, images, and engaging in conversations, still require human input to guide their operations and determine the outcomes.
While the capabilities of generative AI cannot be overlooked, its impact on enhancing productivity is often limited by one significant drawback—decision-making. Current AI systems can assist with various aspects of a job, but they lack the ability to autonomously execute tasks.
For example, while you might ask an AI like ChatGPT to draft an email notifying a client of a delay, it cannot send the email or manage the response that follows. Similarly, although AI can recommend the latest smartphones for video recording, it cannot compare prices or make purchases on your behalf.
Acknowledging this limitation, technology companies developing large language models (LLMs) have begun to refer to these systems as AI agents. Researchers argue that AI agents have the potential to evolve a traditional knowledge-based AI into a more capable action-oriented system that can perform complete tasks without requiring human oversight.
This concept gained traction in late 2024 and is increasingly being viewed as a solution for various workplace challenges. However, questions remain regarding its true transformative potential. This article aims to clarify these queries and explore the multifaceted nature of AI agents.
What is an AI Agent?
Given that this technology is still developing, a standardized definition of an AI agent has yet to be firmly established. IBM describes it as a system capable of automatically executing tasks on a user’s behalf by creating workflows and utilizing various tools. Google, which introduced its AI agent called Project Mariner last year, defines it as a supportive system that assists individuals in completing tasks.
Amazon offers a more detailed definition, explaining that an AI agent is a software program capable of interacting with its environment, gathering data, and performing self-directed tasks to achieve predetermined objectives. While humans set the goals, an AI agent independently determines the most effective actions to fulfill those aims.
In simpler terms, an AI agent is an intelligent system that can take action, rather than merely advising the user on what actions to take.
Breaking Down the AI Agent
An AI agent typically relies on a large language model (LLM) as its core component, augmented by additional elements that allow it to translate knowledge into action. These elements may include sensors, mechanical components, encoders, and integration with other software.
Sensors facilitate the collection of diverse data formats, including visuals, sound, temperature readings, or electronic signals. Mechanical components are used in robotics, enabling physical actions such as lifting or maneuvering objects. Encoders convert various signals into recognizable information for LLMs, while software integration allows these agents to perform specific tasks.
It’s essential to distinguish between AI models and AI agents at this point. While AI models operate from a pre-existing knowledge base, which limits their ability to respond to new inputs or events, AI agents, once connected to relevant systems, can independently gather fresh information and solve problems that existing data do not address. Google’s Project Mariner exemplifies this by interacting with browsers to locate the best smartwatch deals.
Furthermore, AI agents excel in handling complex tasks by utilizing advanced reasoning, effectively breaking larger operations into manageable components. This situational awareness and problem decomposition represent a defining capability of AI agents.
A recent feature from Gemini, called the Deep Research tool, illustrates this functionality. When prompted to explain a specialized topic, the AI develops a multi-step research strategy, segments the subject into smaller units, locates pertinent literature, implements the plan, and compiles the findings into a comprehensive report.
Applications of AI Agents
Technology companies have heralded AI agents as versatile tools applicable across various industries. They can function as voice assistants for devices, manage specific tasks within applications, or be integrated into enterprise systems to detect fraud and enhance operational efficiency.
In specific sectors, AI agents are anticipated to revolutionize traditional practices. In healthcare, they can aid in diagnosing conditions, recommending treatments, and discovering new drugs. The automotive industry may utilize AI agents for developing self-driving cars, while drones equipped with AI could gather and analyze data in disaster zones, providing actionable insights for rescue efforts.
AI agents also show promise in manufacturing through AI-powered robotics, in gaming as developers or non-playing characters, and in education by producing tailored study plans and grading assessments in a manner reminiscent of human evaluators.
Despite the enthusiasm from tech firms promoting AI agents as comprehensive solutions for intelligent automation, current technology largely restricts their functionality to specific tasks rather than as all-purpose tools.
AI Agents in 2025
Looking toward the future, it is crucial to manage expectations regarding what AI agents may realistically achieve in the near term. Their integration into critical sectors such as manufacturing, healthcare, or education remains unlikely this year.
However, 2025 may witness the emergence of AI agents in consumer electronics, desktop applications, and online platforms. Google’s Project Mariner, for instance, is expected to seamlessly integrate with Google Chrome, assisting users with online shopping and file retrieval.
Additionally, OpenAI is anticipated to debut its AI agent soon, potentially enhancing ChatGPT’s functionality, while tools like Anthropic’s Computer Use are also set for broader release, aiding users in everyday tasks.
Eventually, a transition may occur where AI agents can replicate keystrokes, mouse navigation, and other interactions on devices. This could enable tools like the coding agent Devin to autonomously write, test, debug, and deploy code without human input. However, it would be optimistic to anticipate such advancements within the immediate next year.
On the enterprise front, AI agents could increasingly take on organizational duties such as data monitoring, report generation, and process optimization. Moreover, AI’s role in cybersecurity is already being explored, with companies like Meta utilizing AI to enforce compliance and YouTube employing it to catch copyright infringements.
Nonetheless, widespread adoption of AI agents in critical business functions remains improbable this year due to their largely untested nature, leaving concerns about reliability and security. Organizations, particularly publicly traded companies or those with significant investment backing, tend to be cautious when it comes to sensitive data, making them reluctant to embrace this technology.
The Problems With AI Agents
As AI continues to be a hot topic in the tech world, the excitement surrounding AI agents is understandable. However, there are several challenges that need addressing before these technologies can achieve widespread acceptance. If left unchecked, they could also pose certain risks.
One major concern involves bias and discrimination stemming from their training data, which can lead to harmful outcomes. This brings attention to the need for transparency, as AI systems are often complex and intricate, complicating efforts to understand their decision-making processes.
Security and privacy issues are also prevalent. AI agents can be susceptible to adversarial attacks, where malicious inputs are designed to deceive the system. Given their need to connect with various platforms and gather large data sets for task completion, privacy risks are inevitable.
Considering these challenges, AI firms will face a significant task in convincing enterprises and the general public of the benefits of this technology while alleviating concerns about its drawbacks. Regardless, it is clear that AI agents will play a pivotal role in the technological landscape leading up to 2025.
