Welcome to the Era of Agentic AI

If you’ve encountered the phrase “agentic AI” in tech discussions and have been silently agreeing without grasping its meaning—you’re not alone. However, it’s crucial to get acquainted, as agentic AI is rapidly emerging as a significant advancement in artificial intelligence.

Key technology giants are making substantial investments in this burgeoning domain. Google has recently proclaimed the arrival of the “agentic era,” unveiling tools such as a web-browsing research assistant and an AI bot capable of making calls to businesses for you. OpenAI is also in the game, showcasing Operator—an AI agent that can perform tasks for users—and Deep Research, which independently searches the web to gather information. Microsoft has not been sidelined either, releasing Microsoft Discover, an agentic AI tool aimed at researchers. Even mobile devices are beginning to feature agentic capabilities that can send messages, schedule events, and aggregate information from various applications.

So, What Is Agentic AI?

As defined by Google’s Gemini chatbot, agentic AI encompasses systems that function autonomously: they sense their surroundings, establish objectives, devise plans to achieve them, and execute those strategies—all without needing constant human direction. These systems also have the ability to learn and adapt over time, enhancing their efficacy based on feedback and new data.

This marks a progression from conventional generative AI, which is designed primarily to create content—such as text, images, or code—based on input from users. In contrast, agentic AI can take initiative. It has the capability to browse the web, evaluate outcomes, consult multiple sources, and even perform tasks across various applications.

Although the term “AI agents” is frequently used interchangeably, agentic AI is more comprehensive. It encompasses systems that may not be entirely autonomous but still exhibit agent-like characteristics. For instance, OpenAI’s Operator is seen as an AI agent because it comprehends context and can perform tasks like sending messages. Conversely, Deep Research is categorized as agentic AI due to its ability to autonomously collect and synthesize information online.

These functionalities are powered by sophisticated reasoning models like ChatGPT o3 and Gemini 2.5 Pro Preview. Unlike their predecessors, these models can decompose intricate tasks and make inferences, bringing AI closer to human-like problem-solving abilities. Karen Panetta, an IEEE Fellow and engineering professor at Tufts University, clarifies that while generative AI requires retraining to learn new concepts, agentic AI can learn from examples and undertake more goal-focused tasks.

When enhanced with computer vision—affording AI the ability to “see” your screen—agentic AI becomes significantly more powerful and adaptable.

Why the Buzz Now?

Agentic AI isn’t entirely novel. Technologies such as self-driving vehicles and robotic vacuum cleaners serve as early instances of autonomous systems reacting to their surroundings. However, recent innovations have rendered agentic AI more accessible and user-friendly. As individuals grow more accustomed to interacting with AI chatbots like ChatGPT, there is an increasing demand for tools that can automate daily tasks—such as responding to emails or organizing schedules—without requiring programming knowledge.

This transformation is why companies like Google, OpenAI, and Anthropic are heavily investing in agentic AI. It signifies a shift beyond mere novelty chatbots toward tools that could become indispensable in both personal and professional environments. Envision an AI capable of ordering groceries, comparing espresso machines, or planning your next getaway. Google is already experimenting with these functionalities through its AI-driven shopping options.

In the corporate arena, agentic AI is being researched for automating customer service and facilitating real-time financial decision-making. The potential is immense—and expanding.

What Could Go Wrong?

Naturally, with great power comes significant responsibility—and risk.

Empowering AI agents to act on your behalf presents new complexities. What if an agent forwards a confidential email to the incorrect recipient? Or makes an unauthorized purchase? And who bears responsibility if an error occurs?

Karen Panetta raises a vital inquiry: “If I’m an executive and I employ an AI agent to make a planning decision that incurs financial loss, am I legally accountable?” These concerns are currently being discussed, and companies are enacting safeguards. For example, OpenAI’s Operator will refrain from taking screenshots while in human override mode and currently cannot perform banking operations.

However, as agentic AI gains traction, will users become overly trusting? If we depend on AI to define terms or complete tasks, might that undermine our own analytical thinking abilities? Some researchers are already concerned that excessive reliance on AI could result in decreased intellectual engagement among individuals.

Then there’s the matter of privacy. For an AI agent to manage your emails or calendar, it must have access to sensitive information. Are we prepared to relinquish that level of control?

And we must also consider the potential for malicious use. With powerful computing more affordable than ever, malicious individuals could exploit agentic AI to execute sophisticated cyber attacks.