AI agents are being touted as the next big revolution in artificial intelligence. They’re promising, innovative, and poised to reshape how we interact with technology. But here’s the catch: there’s no universally accepted definition of what an AI agent actually is. Despite all the buzz, the concept remains somewhat fluid, leaving both experts and enthusiasts debating its boundaries and potential.
Defining the AI Agent: A Moving Target
At its core, an AI agent can be described as software powered by artificial intelligence designed to carry out tasks autonomously. These tasks might once have been handled by a human employee in roles like customer service, IT support, or HR. However, the range of an AI agent’s responsibilities is broad and evolving. You give it an objective, and it performs the required actions, sometimes working across multiple systems and delivering results that go far beyond answering simple questions.
For instance, consider Perplexity’s AI shopping assistant, released just in time for the holiday season. It helps users find gifts tailored to their needs. Similarly, Google recently unveiled its first AI agent, Project Mariner, which streamlines tasks like booking flights and hotels, shopping for household goods, and even discovering new recipes. These examples illustrate how AI agents are moving from the realm of theoretical potential to practical applications.
Sounds straightforward, right? Well, not quite. Even within the tech world, the definition of an AI agent varies significantly. Google, for example, frames them as task-based assistants—tools that cater to specific needs like helping developers write code or assisting IT professionals in troubleshooting issues. Meanwhile, Asana envisions AI agents as virtual co-workers, capable of tackling assigned tasks like any productive team member.
Startups like Sierra, co-founded by former Salesforce co-CEO Bret Taylor and Google veteran Clay Bavor, see agents as tools for transforming customer experiences. These agents go beyond simple interactions, solving complex problems and facilitating meaningful actions that older-generation chatbots couldn’t touch. This diversity of interpretations reflects the nascent stage of AI agents, and it’s precisely why there’s so much room for confusion and innovation.
The Role of Autonomy in AI Agents
Despite the lack of consensus, most experts agree on one thing: an AI agent should exhibit a degree of autonomy. Rudina Seseri, founder and managing partner at Glasswing Ventures, sums it up well: “An AI agent is an intelligent software system designed to perceive its environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously.” These systems often rely on multiple AI/ML technologies such as natural language processing (NLP), computer vision, and machine learning to operate effectively in dynamic settings—sometimes alongside human users, sometimes entirely on their own.
The end goal? To reduce the need for human involvement in repetitive or complex tasks while enhancing efficiency and scalability. Aaron Levie, co-founder and CEO of Box, takes an optimistic view of this trajectory. According to Levie, as AI becomes more sophisticated, agents will handle increasingly complex tasks. He highlights key areas of improvement—like better GPU performance, advances in model efficiency, and infrastructure enhancements—that will fuel the rapid evolution of AI agents.
The Challenges of Building Truly Autonomous AI Agents
But not everyone is as optimistic about the speed of progress. MIT robotics pioneer Rodney Brooks offers a more cautious perspective. He points out that AI faces tougher challenges than most technologies and won’t necessarily follow the exponential growth curve predicted by Moore’s Law.
“When a human sees an AI system perform a task, they immediately generalize its competence,” Brooks explains. “They overestimate what the system can do based on one specific performance.” This tendency to overestimate capabilities creates unrealistic expectations for AI agents, especially when it comes to tackling unpredictable, multi-step problems.
One major hurdle is the complexity of integrating AI agents with legacy systems. Many older platforms lack the API access necessary for seamless integration, making it challenging for AI agents to operate across multiple systems. While tech improvements are steadily addressing these issues, achieving true cross-system autonomy remains a distant goal.
The Vision of Fully Autonomous Agents
David Cushman, a research leader at HFS Research, sees today’s AI agents as capable assistants rather than fully independent workers. These agents are excellent at handling defined tasks within a framework of user-defined goals, but the leap to full autonomy—where AI operates independently at scale—is still out of reach.
“It’s the next step,” Cushman says. “It’s where AI operates effectively without human intervention. Today’s AI is about keeping humans in the loop, but tomorrow’s AI will take humans out of the loop entirely, applying true automation.”
This transition to autonomous agents will require a robust tech stack. Jon Turow, a partner at Madrona Ventures, believes the growing demand for AI agents calls for a specialized infrastructure. “Developers want the underlying platform to ‘just work’ with scale, performance, and reliability,” Turow wrote in a recent blog post. He envisions a future where reasoning capabilities improve, workflows are guided by cutting-edge models, and developers can focus on refining their products without worrying about foundational AI systems.
The Road Ahead: Multi-Model Collaboration
Another key insight comes from Fred Havemeyer, head of U.S. AI and software research at Macquarie US Equity Research. Havemeyer argues that no single large language model (LLM) is currently capable of managing the multi-step reasoning needed for truly autonomous agents. Instead, he predicts that the most effective agents will rely on a collection of models, each specializing in different tasks.
“Think of it as an automated supervisor,” Havemeyer says. “One layer delegates tasks to the most suitable models, ensuring each component operates at peak efficiency.” This multi-model approach underscores the complexity of creating AI agents that can handle diverse challenges.
Havemeyer’s vision for the future involves agents that can independently break down abstract goals into actionable steps, reasoning through problems without human guidance. But achieving this level of sophistication will require significant breakthroughs in AI reasoning, task coordination, and infrastructure development.
A Promising Future with Clear Challenges
The excitement around AI agents is well-deserved. They represent a bold step forward in automating tasks and redefining workflows across industries. However, the technology is still in its infancy. As Rodney Brooks reminds us, AI progress is often slower and more complex than we anticipate.
For now, AI agents are promising tools—helpful assistants that excel in well-defined roles. But the ultimate goal—fully autonomous agents capable of tackling abstract goals and navigating unforeseen challenges—remains on the horizon. It’s a vision that will require not just technological breakthroughs but also careful consideration of the ethical and societal implications of empowering machines with such autonomy.
In the meantime, businesses and developers should embrace the capabilities of current AI agents while keeping an eye on the innovations that will shape their future. The journey to truly autonomous AI agents has just begun, and the possibilities are as exciting as they are daunting.
