You’ve probably heard a lot about ChatGPT, Google’s Gemini, image generators and AI writing tools. But there’s a new term making the rounds: agentic AI. And while it might sound like another buzzword, it’s not a new invention. 

Recent advances, however, have made it far easier to build, deploy and interact with these kinds of systems. Some of them you might have already seen at work, like customer service banking bots, self-driving cars and smart home assistants. 

If you’re using Perplexity in the US as a Pro subscriber, a perfect example is its “Buy with Pro” feature. Rather than assisting with your shopping and handing you off to a retailer, it collects your preferences, processes the transaction (sometimes even selecting the best available retailer) and uses your stored payment and shipping information to complete the order.

Experts say it’s time to start paying attention to what these AI agents are capable of doing on their own, though widespread use across industries will take time before AI agents become mainstream.

Unlike AI chatbots, which often require explicit instructions at each step, AI agents can break down complex objectives into smaller, manageable actions. So instead of simply responding to your questions or prompts, agentic AI is designed to take initiative. That means understanding its environment, making decisions and acting without human direction at every step.

So what does that look like in practice, and how is it different from what artificial intelligence is already doing? I spoke to several experts and will break down everything you need to know about agentic AI — including whether it can be trusted.

From passive tools to proactive agents

Agentic AI systems aren’t passive tools waiting for input. They operate in a cycle to sense the environment, decide what to do, and then act. That structure makes them more autonomous and lets them take on complex, goal-oriented tasks across multiple systems.

“Agentic AI…is now making this sort of sense-decide-act loop available to everybody,” Peter Stone, professor at the University of Texas and chief scientist at Sony AI America, told me. “Rather than waiting for the input-output behavior, you’re able to task a program with sensing the world, deciding what to do and actually acting.”

Ankur Patel, CEO and founder of enterprise agentic AI company Multimodal, called it “a fundamental shift from reactive tools to proactive systems capable of complex decision-making.” He gave an example of a loan underwriter who might otherwise spend hours cross-referencing pay stubs, tax returns and credit reports. 

“The AI agent automatically ingests and validates hundreds of data points from diverse sources. Think bank feeds, HR systems and government databases, while flagging inconsistencies like mismatched employment dates,” Patel told me.

In other words, it’s not mere automation. “Agentic AI connects complex, multisource inputs with internal rules or manuals, and gives accurate, critical outputs in much shorter time frames,” Patel explained.

What makes agentic AI different from generative AI and chatbots?

Generative AI creates content such as text, images, music and even videos, based on what it learned during training and your prompt. Agentic AI can use those same models, but adds a layer of autonomy with reasoning and planning to proactively achieve goals through a sequence of actions.

A generative AI tool might write you a vacation itinerary. AI agents could plan the trip, book your flights, reserve the hotel and even rebook everything if your flight gets delayed or canceled.

Large language models, like ChatGPT or Claude, can become agentic when connected to external tools, sensors or APIs. This ability to interact with the world (either physical or digital) is what makes the difference.

While systems like ChatGPT and Siri are designed to answer questions, agentic AI is built to solve problems.

“Chatbots answer questions. Agentic AI solves problems by turning insights into outcomes,” Patel said. That means orchestrating tasks across platforms. “For example, it can verify documents, assess risks and even trigger real-world actions like loan approvals or insurance payouts.”

Automation vs. augmentation

Like most new tech, agentic AI raises concerns about jobs. Will it replace workers, or help them do their jobs better? Stone said the answer isn’t simple. 

“Usually, when people say automation, they’re thinking of replacing jobs. When people say augmentation, they’re thinking of changing jobs, making them more efficient,” Stone said.

He compared it to the transition from hand-washing dishes in a restaurant to using a dishwasher — there’s still a human in the loop, but they’re doing less of the repetitive labor.

Another relatable example is correspondence. While writing letters by hand and sending them via snail mail might trigger nostalgia in romantic folks like me, we now send messages and emails instantly from smartphones.

Patel agreed that agentic systems free people up from the grunt work. “It’s unfortunate that a lot of man hours even today are spent on drudgery,” he said. “Good AI can take care of them without needing much supervision.”

For Patel, the bigger risk is falling behind. “The question isn’t ‘will AI take my job?’ It’s ‘will I be working alongside AI or getting outpaced by those who do?'”

While that might sound daunting to anyone hesitant about the shift, AI is advancing fast enough that it feels inevitable.

Where you might see agentic AI in action

Enterprise software vendors are already rolling out agentic systems in industries like:

• Robotics: Autonomous robots that can navigate and perform tasks in complex environments.
• Software development: AI agents that can write and debug code independently.
• Customer service: Advanced chatbots that can resolve complex issues without human assistance.
• Supply chain: AI agents that manage inventory, forecast demand and optimize delivery routes.
• Manufacturing: Systems that monitor equipment, detect defects and streamline production lines.
• Cybersecurity: Agents that detect threats, isolate risks and respond in real time.
• Insurance: AI agents that process claims, assess risk and draft responses.
• Banking: Systems that verify income, credit and approve loans.
• Healthcare: Tools that flag anomalies, suggest diagnoses and draft notes.

AI agents in these industries process documents, extract data, flag inconsistencies and route information with minimal human intervention.

But, you don’t have to work in any of these sectors to notice it. Opera’s agentic web browser and Google’s new agentic search, called AI Mode, aim to help you go from inspiration to purchase without clicking through pages of results. AI assistants that can book your travel, manage your inbox or compare online deals are all signs of what’s coming in the consumer sector as well. Even Microsoft is adding an AI agent to Windows that can change system settings for you.

Patel says everyday users should care for three reasons: “First, it gives people their most precious resource back — time. Second, it vastly improves customer experience. Third, it prevents costly human errors.”

That said, there are still limitations. AI agents struggle in open-ended or unpredictable environments, especially when tasks lack clear structure or context. They also depend heavily on well-formed prompts or goals. Meaning, vague input can lead to irrelevant or faulty actions.

Can it be trusted?

Autonomy brings benefits, but also risks. When systems make decisions or take action without supervision, what happens if something goes wrong? And who is responsible? Is it the person using the AI, or the company/developers that built it? Legal dilemmas continuously expand with these AI advancements.

Stone also warns that the risks aren’t hypothetical. 

“The new type of risk… is not a person acting incorrectly or irresponsibly as a result of what the AI advises, but rather the AI system actually taking a dangerous action,” he told me. 

Say you let an autonomous car drive itself, it can do more than just suggest a route and cause harm if it malfunctions or has you drive in circles in a parking lot like this unfortunate passenger.

The stakes depend on what the AI is allowed to do. Booking a low-cost flight? Low risk. Accessing medical records or spending thousands of dollars? Much higher. 

“The risk is directly related to the space of actions and the agency or autonomy that you give to the agent,” Stone emphasized.

Patel pointed out that safeguards are essential, especially in regulated industries. “To truly trust AI, it needs to have detailed audit trails and decision logs, process documentation, confidence scoring, and the ability to route decisions to humans where absolutely necessary,” he said.

What’s next?

While the hype around agentic AI is rising fast, don’t expect to hand over your entire life to AI agents anytime soon. It will take years for most agentic AI systems to be tailored to specific industries or problems, not one-size-fits-all assistants.

“There’s a real chance that by demanding perfection from autonomous agents, we’re missing an opportunity to do a lot better than the status quo,” Stone said. “I think we need to accept that there are going to be mistakes. But they’re going to get better over time.”

And so, the direction is clear. We’re moving from AI that chats with us to AI that does things for us. Add robotics to the mix, and it’s a whole new ballgame.

FAQs

What is agentic AI?

Agentic AI is artificial intelligence that can independently make decisions and take actions to achieve a goal. Instead of waiting for step-by-step commands, AI agents decide what needs to be done and take action across systems with minimal human involvement.

What are the key characteristics of agentic AI?

• Autonomy: It can make decisions and act without constant human input.
• Planning: It can develop and follow plans to meet goals.
• Adaptability: It can adjust strategies based on feedback and context.
• Learning: It improves performance over time through experience.