Agentic Ai What You Need To Know About Ai Agents

Emily Johnson

-Mar 12, 2026, 8:14 PM

agentic ai what you need to know about ai agents

Your guide to the latest wave of AI technology and who at CSAIL is working on it. If you’d like to learn more about leveraging the Agentic AI research happening at CSAIL or MIT, please get in touch with your CSAIL Alliances Client Relations Coordinator or with our Associate Director Glenn... 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... 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. Agentic AI refers to artificial intelligence agents that work autonomously in real-world and virtual settings. Unlike traditional AI assistants that only respond to human prompts, these systems utilize advanced AI techniques to make decisions, take actions, and achieve goals independently. Agentic AI can even learn from new information and adjust its strategy as situations evolve, which allows for flexibility when confronted with complex issues. Agentic AI traces back to the broader development of artificial intelligence. The field’s foundations were laid by pioneers like Alan Turing, who proposed that machines could exhibit intelligent behavior and, ultimately, learn from experience.

Early AI efforts in the 1950s and 60s focused on creating programs that mimicked human decision-making within limited contexts. In the 1980s and 90s, robotics and computer vision advancements led to agent-like qualities, enabling machines to interact with physical environments. However, these early “agents” still had limited autonomy. The modern era of agentic AI began in the 21st century with the advent of machine learning (ML), neural networks, and reinforcement learning (RL). These technologies empowered systems to learn from existing data, adapt to change, and pursue goals with minimal human intervention. The rise of autonomous vehicles, robotic process automation (RPA), and intelligent personal assistants (IPAs) like Siri and Alexa demonstrated the move toward agentic capabilities.

Multi-agent systems (MASs), where independent AI agents collaborate or compete, also played a crucial role. That leads us to today, where agentic AI is predicted to be one of the top technology trends for 2025. Agentic AI gathers data, autonomously makes decisions, and adapts to new information. We’ll explain how in greater detail below, with examples of agentic AI in real-world applications. Agentic AI perceives its environment and collects any data it considers useful. This data can be in the form of text, images, or real-world information.

Agentic AI utilizes large language models (LLMs) and natural language processing (NLP) to gather this data, similar to the way a self-driving car uses its sensors to “see” the road or a chatbot interprets... By Zack Huhn, Enterprise Technology Association The era of AI agents, or what many now call agentic AI, is here. These systems are reshaping how businesses, governments, and individuals interact with technology. But with the hype comes confusion. Let’s break it down: what are AI agents, what makes AI agentic, and why does it matter for business and technology leaders?

At their core, AI agents are systems that act autonomously to achieve goals. Unlike traditional software, AI agents don’t just follow static instructions,they perceive, reason, plan, and act in dynamic environments. A simple example? Think of a chatbot that not only answers questions but books appointments, sends follow-up emails, and adapts its tone based on user sentiment. But AI agents go far beyond that. They can:

Execute complex, multi-step tasks with minimal human input. The agentic era of artificial intelligence has arrived. AI agents are capable of operating independently and without continuous, direct oversight, while collaborating with users to automate monotonous tasks. Based on the same large language models that drive popular chatbots like ChatGPT and Google Gemini, agentic AIs differ in that they use LLMs to take action on a user’s behalf rather than generate... In this guide, you’ll find everything you need to know about how AI agents are designed, what they can do, what they’re capable of, and whether they can be trusted to act on your... Billed as “the next big thing in AI research,” agentic AI is a type of generative AI model that can act autonomously, make decisions, and take actions towards complex goals without direct human intervention.

These systems are able to interpret changing conditions in real-time and react accordingly, rather than rotely following predefined rules or instructions. AutoGPT and BabyAGI are two of the earliest examples of AI agents, as they were able to solve reasonably complex queries with minimal oversight. AI agents are considered to be an early step towards achieving artificial general intelligence (AGI). In a recent blog post, OpenAI CEO Sam Altman argued that, “We are now confident we know how to build AGI as we have traditionally understood it,” and predicted, “in 2025, we may see... Marc Benioff hailed AI agents’ emergence as “the third wave of the AI revolution” last September. The “third wave” is characterized as generative AI systems outgrowing being just tools for human use, instead, evolving into semi-autonomous actors capable of learning from their environments.

Accelerate decision-making agility, strengthen resilience, and save costs with a single, unified platform for both IT and security—powered by AI and real-time intelligence. Accelerate decision-making agility and save costs with autonomous operations, integrated IT and security, and comprehensive endpoint management. Strengthen resilience through continuous vulnerability monitoring, real-time risk scoring and prioritization, and integrated remediation. Strengthen resilience using real-time threat hunting, continuous detection, autonomous response, and deep integrations. Streamline workflows, increase automation, and enhance security with platforms and tools you already use. If you’ve been keeping up with the artificial intelligence (AI) world, you’ve probably heard of the terms AI Agents and Agentic AI.

On the surface, they could easily pass off as another set of buzzwords, but they’re actually two different kinds of AI systems that are going to completely change the way we work, build, and... So, what are they? How are they different? And why should you care? In this guide, we’ll break down both concepts in simple terms, highlight their real-world uses, and explore what the future holds for each. Go beyond task-based automation.

Integrate Agentic AI into your systems with Dextralabs and unlock smarter decision-making across your tech stack. AI agents are self-contained computer software programs that are meant to act in accordance with a particular set of tasks by sensing their surroundings, making choices, and behaving towards an objective. While the term is technical, you interact with AI agents more than you realize in terms of chatbots, recommendation systems, or even GPS navigation applications. These Ai Agents are often constructed for specific, narrowly defined purposes and excel at repetitive tasks and mundane assignments. For example, a customer support chatbot answers user questions in real time, while a code assistant such as GitHub Copilot prompts you with code snippets as you are typing. What differentiates AI agents from other entities is that they are reactive.

You may have heard about “Agentic AI” systems and wondered what they’re all about. Well, in basic terms, the idea behind Agentic AI is that it can see its surroundings, set and pursue goals, plan and reason through many processes, and learn from experience. Unlike chatbots or rule-based software, agentic AI actively responds to user requests. It may break activities into smaller tasks, make decisions based on a high-level goal, and change its behavior over time using tools or other specialized AI components. To summarize, agentic AI systems "solve complex, multi-step problems autonomously by using sophisticated reasoning and iterative planning." In customer service, for example, an agentic AI may answer questions, check a user's account, offer balance... So, agentic AI is "AI with agency”.

Given a problem context, it sets goals, creates strategies, manipulates the environment or software tools, and learns from the results. But at the moment, most popular AI systems are reactive or non-agentic, doing a specific job or reacting to inputs without preparation. For example, Siri or a traditional image classifier use predefined models or rules to map inputs to outputs. Instead of long-term goals or multi-step processes, reactive AI "responds to specific inputs with pre-defined actions". Agentic AI is more like a robot or personal assistant that can handle reasoning chains, adapt, and "think" before acting.

Agentic Ai What You Need To Know About Ai Agents

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