A clamorous revolution powered by Artificial Intelligence (AI) has been restructuring the way the human world operates for quite a while now. Among its latest developments is agentic AI, an autonomous AI system that, by eliminating the need for human intervention altogether, is taking things one step further.
As it continues to mature thus and redefine the way we, as humans, interact with technology (or vice versa), it is imperative we give the system a comprehensive look. This is so we know how it differs from its traditional counterparts, especially generative AI. Read on as we try and achieve the same in this blog, one key aspect at a time!
Agentic AI, in simple language, is an AI system that’s capable of making decisions without human interference. Named so after the term agent, these systems are programmed to act just like humans do, no matter the situation. These can learn, adapt, and act based on real-time information received from the environment they are operating in.
Being goal-oriented, they can change their approach towards something based on the changing circumstances. Their ultimate aim is to meet the end goals, and they have all the autonomy in the world required to revise their strategies accordingly without human oversight. This is also what sets them apart from traditional AI systems (case in point – generative AI) that are more rule-based and restricted in their approach.
Let’s take an extensive look at all the key characteristics of agentic AI in order to understand its true potential…
Agentic AI operates independently and requires no human help to make decisions and perform tasks. It is autonomous enough to navigate real-world environments and make actionable judgments based on the information it thus perceives.
These AI systems are programmed to pursue specific goals. If any changes are made in the course of action specified to achieve them, these are capable of altering their strategies to fit the situation without requiring any inputs from humans.
For instance, if an autonomous delivery drone is supposed to deliver a package to a specific location, it will do so, come what may. Be it weather-related disturbances or any other obstacles, agentic AI will quickly adapt to them and make appropriate changes in its plans to complete the task successfully.
Agentic AI is devised to be adaptable. It learns from its experiences, both new and old, and adjusts its actions in accordance with them. Credit for this in part goes to machine learning, which helps it recognize patterns in data and change its decision-making expertise to match them.
For instance, an AI-driven car will learn to navigate unfamiliar routes by driving through them, i.e., by gaining knowledge through experience.
Agentic AI is capable of assessing different courses of action and choose one that suits its purpose best. This decision-making ability acts as a tool of great significance to handle environs that are susceptible to change, financial markets for one.
These systems cannot operate without meaningful interactions, both with their immediate environments and human operators. This is not to say that they rely on humans to achieve their goals. It, in fact, implies that they help humans achieve mutual goals, if any.
For instance, robotic surgeons, in a healthcare setting, not only assist doctors in performing the operation but also in getting real-time insights on its status.
The difference between agentic AI and generative AI is too profound to not make a note of. For a lot of factors mark off one from the other. Some of them are listed below for your understanding…
Agentic AI is programmed to make decisions and act on them autonomously. It focuses on interacting with the environment it operates in to perform tasks or solve problems based on real time data. Generative AI, on the contrary, only focuses on creating content, including texts, images, music, etc., based on the prompts received. It does not take any actions on its own.
The difference between the two, in short, is synonymous to the contrast between managing a supply chain and giving detailed reports on one.
Agentic AI engages with the outside world to meet its goals. A self-driving car navigating its way through traffic on the basis of its own judgements can serve as a good example of this.
On the other hand, generative AI only interacts through content creation. It writes essays or creates artworks based on prompts received from the outside world. That’s all that there is to its interaction with the environment it operates in.
While agentic AI learns through its experiences in the real world, generative AI uses patterns from datasets to learn and create new content.
For instance, an agentic AI chatbot answers customer queries, while learning from its interactions with them. A generative AI chatbot, in contrast, provides personalized responses to user queries. It responds in accordance with customer preferences.
Agentic AI includes autonomous agents such as automated vehicles, personal assistants like Siri, and robots that perform actions. It thus gets deployed in operational environments.
Generative AI, conversely, includes text generated by GPT-3 or images created by DALL-E. It gets deployed in creative sectors like entertainment, marketing etc., for the same reason.
Agentic AI has a host of benefits to offer in a world that’s slowly and steadily increasing its dependence on machines in every sphere. These include…
Since these systems are capable of autonomously taking charge of situations, these significantly enhance productivity and efficiency across industries. As machines in control of their operations, these aren’t susceptible to fatigue and get tasks done faster than humans.
In supply chain management, for instance, agentic AI robots can accelerate the movement of goods, all while monitoring inventory and reporting shortage therein, if predicted any. This not only eliminates the need for human input, but also cuts down on manual efforts, enhancing overall efficiency.
Humans are bound to make mistakes. Machines, on the other hand, aren’t prone to errors or biases in judgements. Those powered by agentic AI thus can get things done with accuracy, minimizing human errors
In the medical sector, for instance, AI systems can help assess medical reports with great precision. They can, in fact, detect complications even before a doctor does.
Though the initial investment required to install agentic AI systems is high, the returns these bring in response make up for it through and through. As these automate a host of tasks in their wake, these significantly lower the labour costs incurred too.
For example, AI chatbots can handle a slew of customer inquiries at once. This not only helps a business cut down on its human capital needs but also helps human customer care executives direct their focus on more complex issues.
Agentic AI is capable of offering personalized services to meet individual preferences. In an e-commerce setup, for example, agentic AI helps make product recommendations on the basis of a customer’s browsing history or past purchasing behaviour.
Despite the benefits listed above, a lot can go wrong when it comes to adopting agentic AI. Let’s look at all the possible challenges these systems pose and think of ways to address them while at it…
When it comes to making moral or ethical choices, these AI systems seem to be lagging. For example, when a self-driving car gets faced with a choice between avoiding a pedestrian or risking the safety of the passenger, it is bound to harm someone in the process. This raises questions of accountability. Would we get the car punished in the court of law for the harm caused or hold its manufacturer/developer responsible?
If this wasn’t enough, these systems are also prone to biases, that of their developers. DeepSeek, for instance, has been making headlines around the world for its biased data favouring China. This data is not only capable of causing societal inequalities, but it also brings the moral and ethical considerations of Artificial Intelligence as a whole into question.
Just like everything else, these AI systems are also prone to cyberattacks. A self-driving car, for instance, might get hacked, leading to dire consequences for the passengers travelling in it.
This warrants the need for a robust security mechanism that helps these systems evade malware attacks/threats of any kind.
While automation significantly enhances operational efficiency on one hand, it leads to a serious loss of job opportunities on the other. For tasks such as driving, data entry, or customer service no longer require human involvement. These can very well be performed by AI systems, leading to unemployment.
Some regulatory changes in this regard are thus needed, so humans aren’t compelled to take a back seat in a world they helped develop and build.
Rome wasn’t built in a day, and so weren’t agentic AI systems. For these require heaps of data, computing power, technological know-how, and algorithms to develop and further operate. The resultant complexity and high costs of development can make them irrelevant for SMEs, limiting their reach.
Agentic AI is being increasingly deployed across a range of industries. Some of its most notable modern-day applications are…
The most popular example of agentive AI is self-driving cars. These vehicles can navigate routes, follow traffic rules, and avoid hindrances, all on their own without any sort of human involvement. Automating driving thus, these are revolutionizing the way humans commute and significantly reducing our dependence on fuels.
In the medical sector, Agentic AI is being used for robotic surgeries, health diagnosis, and offering personalized treatment plans. Owing to their precision and analysing powers, these are ruling the sphere, even leaving their human counterparts behind in most regards.
Their integration into smart homes and the Internet of Things (IoT) is making it easy for humans to manage their homes. With help from smart assistants like Amazon Alexa and more, humans can get all their household tasks, from handling of devices to grocery shopping, done at the click of a button.
Agentic AI is primarily used for algorithmic trading, financial advising, and investment planning in the finance sector. For these systems can analyse data and make market predictions at the speed of light.
We all come across AI chatbots and assistants as we browse through the web. These bots are well-equipped to resolve our queries, making customer service and support what it always intended to be – a 24/7 service to the customers.
The future of agentic AI looks quite promising if the sundry challenges it has the potential to pose are taken care of. Some key directions it might then take are as follows…
Conclusion
Agentic AI is at the cusp of becoming a technological marvel owing to its exceptional ability to reshape the world and enhance the life of humans living in it. However, as we move towards a future where it plays a central role, it’s important we build frameworks to govern its development and functioning.
For innovation without responsibility and accountability is no less than a monster on the loose. It has the potential to become hazardous for the society it should otherwise serve and completely take over the world. Let’s steer clear of that and let agentic AI unlock its full potential to achieve human goals – ‘human goals’ being the keyword.
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