Nowadays, Agentic AI has become a leading paradigm in modern artificial intelligence. Some experts even call it the third wave of AI after predictive and generative AI, heralding it as “the new AI workforce” for businesses. It’s also the next big step beyond chatbots and content generators. Major companies are taking notice (and so are investors, over $2 billion has flowed into agentic AI startups in the last two years. As a mid or senior developer, you might be wondering what all the fuss is about. In this beginner's guide, we will explain what is Agentic AI and how it functions, with an emphasis on real-life examples from the IT sector. The tone is informal, and the aim is to bring you up to speed with this new technology without the hype.
Agentic AI will operate as a virtual colleague in workplaces, independently managing tasks and decision making. It's a transition in the way that AI supports organizations, breaking away from mere chat answers toward executing actions in the real world. To engineers, this creates new opportunities and new challenges, in creating systems capable of action independently.
What Is Agentic AI?
Agentic AI is an artificial intelligence system capable of independently achieving objectives with minimal human intervention. Rather than simply applying pre-coded rules, an agentic AI applies sophisticated models (often large language models, or LLMs) to simulate human-like decision-making in real time. Practically, this translates into an AI agent perceiving its environment, deciding independently, and acting on tasks to reach an objective goal. It's as if you have a junior co-worker or aide who codes that you tell it what you want, and it decides how to do it. The word "agentic" literally refers to agency – the ability to act independently with intent.
To contrast with traditional software or even earlier AI: older systems operate within fixed constraints and require explicit instructions for each step. In agentic AI, the behavior is goal-driven and adaptive. The AI agents coordinate with each other (often in a multi-agent setup) via an orchestration layer to divide and conquer complex problems. Crucially, they maintain longer-term objectives and adjust their plans based on feedback, without a human telling them exactly how.
Imagine a concrete example, a standard AI chatbot could provide information on weather or flights to you. But an agentic AI travel assistant, with a high-level objective of organizing your holiday, could independently look for places to go, choose the optimal time depending on your requirements, and even reserve flights and accommodations for you. Essentially, it's AI that not only provides answers, but does things.
What’s the Difference Between Agentic AI and Generative AI?
By this point, you've probably employed generative AI (such as ChatGPT) that creates content on request. It's only natural to wonder: we understand what generative AI is, but what's Agentic AI in contrast? The main distinction lies in initiative and autonomy. A generative AI is reactive – it gives you back an answer or creation (text, image, code, etc.) for your input. It will do nothing unless you prompt it. By contrast, agentic AI is active – it can work continuously towards an objective, determining what to do next without constant direction or supervision.
Essentially, generative AI is about generating information upon request, while agentic AI is about deciding and acting on their own. That distinction is key to seeing what Agentic AI is aside from yet another buzzword. Agentic systems use generative AI behind the scenes (particularly LLMs for reasoning), but couple it with decision-making logic, tool usage, and recall in order to act more like an independent problem-solver. Some early examples of agentic AI in practice are driverless cars, autonomous aircraft, and intelligent virtual assistants that are capable of performing tasks (rather than just talk).
What are Some Real-World Examples of Agentic AI?
With the concept behind us, let's examine some real-world examples demonstrating what is Agentic AI actually capable of in the real world. These examples, taken from the IT sector and elsewhere, illustrate how autonomous agents are employed today:
Scientific Research Assistant (Genentech)-
Biotech firm Genentech constructed an agentic AI framework to automate segments of its drug discovery research. The AI agents synthesize challenging research tasks into step-by-step workflows, modify their strategy at every step based on data evaluation, and interact with in-house databases. This allows scientists to delegate time-consuming search and validation procedures, accelerating innovation.Software Engineering Agent (Amazon) –
Amazon developers implemented an AI agent to perform the drudge work of updating older Java apps. In 2024, one agent based on the Amazon Q platform migrated tens of thousands of internal applications from Java 8/11 to Java 17 automatically. It scanned code iteratively, applied modifications needed, executed tests, and delivered updated codebases – all far quicker than would have been possible manually. This autonomous AI utility saved programmers vast amounts of time and minimized mistakes on a grand scale.Customer Service Copilot (Rocket Mortgage) –
The financial company Rocket Mortgage developed an artificial intelligence (AI) support agent that assists customers in navigating home financing. Executed on an agentic platform (Amazon Bedrock Agents), it consumes 10 petabytes of financial data and interacts with customers to suggest customized mortgage options in real time. The agent can even modify customer accounts or make approvals within defined boundaries. The outcome is quicker query resolution and a more streamlined, always-on customer service experience.
These examples illustrate how agentic AI isn't science fiction, it's already doing coding tasks, data-intensive research, and intricate customer interactions. There are even more prototype examples: for example, the company Cognition released an AI software engineer named Devin in 2024 that was developing an agent able to write code independently. In experiments, Devin was able to fix independently between 14% of actual GitHub bugs, approximately twice the amount that a typical chatbot was able to. The pattern is obvious, from IT automation to autonomous cars and drone delivery robots, reasoning and acting agents are quickly coming into being.
What are the Benefits of Agentic AI?
Why are tech executives so enthusiastic about agentic AI? The attraction is that it has the potential to turbocharge productivity and do work previously requiring constant human intervention. A well-crafted agentic AI can execute multi-step processes end-to-endconsider automating software rollouts, watching over cybersecurity threats and acting on them, or processing mundane HR or IT support requests without inconveniencing a human. This holds out enormous gains in efficiency. Indeed, Gartner analysts anticipate a 33× expansion of enterprise software that embeds AI agents between 2024 and 2028 (from less than 1% of applications to 33% of applications). By 2028, they foresee no less than 15% of routine work choices being exercised independently by agentic AI, compared to basically 0% today.
It's not only speculation by analysts, numerous organizations are already doing so. Deloitte has labeled 2025 as an eventual "tipping point": they predict 25% of firms that apply generative AI will initiate agentic AI projects in 2025, reaching 50% by 2027. Early movers are already seeing concrete gains across three critical areas: enhanced productivity, reduced costs, and accelerated innovation cycles. For instance, the agents aren't fatigued or bored – they can work through mundane tasks 24/7, unshackling human professionals from drudge work to concentrate on innovative or sophisticated work. According to a survey, leading use cases driving the adoption of agents are research/summarization, productivity personal assistants, and customer service automation, followed by code generation and data transformation.
From the point of view of a developer, agentic AI tools can be intelligent copilots that support your team. They could do the initial pass at debugging, automatically do QA tests, or watch systems and repair things in advance. In essence, agentic AI can be an indefatigable junior engineer or analyst that multiplies your capacity. And with the progress in LLMs and reasoning methods, such agents are able to adjust to unexpected situations instead of failing when an event does not go as anticipated (a huge improvement over brittle scripts).
To Conclude
In this article, we wanted to clarify what is Agentic AI and why it's causing all the hype. In short, agentic AI systems are independent agents that not only answer questions but also can plan, act, and learn to reach goals, acting nearly like digital co-workers. For engineers and developers, this is a thrilling frontier: it allows creating AI-powered modules that can accomplish sophisticated tasks with little supervision, whether it is coding, managing infrastructure, or engaging with end-users. Examples from real-world applications at Genentech, Amazon, Rocket Mortgage and others demonstrate that the technology is already taking effect, automating processes and complementing teams in the IT sector.
Implementing agentic AI is not without a learning curve and a sense of responsibility. The vision for 2025 offers an accelerated growth in AI agent usage across businesses, so this is an ideal time for seasoned developers to learn about agent frameworks (such as LangChain, IBM's orchestration frameworks, or cloud services for developing agents) and best practices in safely implementing them. By learning about the strengths and weaknesses of agentic AI, you can more effectively position yourself to use this "third wave" of AI in your own work. It's a changing field, but one thing is certain: agentic AI has the power to revolutionize how we work, making the question from "can AI solve this problem?" to "can AI handle this problem for me?" – and more and more often, the answer will be yes.
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