Komal Saim April 20, 2026
Komal Saim April 20, 2026
Artificial Intelligence is not a new concept at all - just not as people used to imagine it. It is not about the world being conquered by robots. Rather, AI quietly runs in the background of our daily lives. In the case of online searching, you receive instant and relevant recommendations. As you browse, products appear to be customized to what you like. You open a tool, and it assists you in writing, summarising or organising your thoughts easily.
This is the way the majority of us are dealing with AI at present - you ask something, and it will provide you with an answer. Easy, quick and convenient. Nevertheless, in recent times, there has been a change.
AI no longer serves to provide more or faster answers. It is starting to go past that, not just responding, but getting more to do with what you request. It is no longer halting at an answer but it is beginning to help in accomplishing tasks and directing the subsequent path.
And here is where the idea of Agentic AI starts to develop.
In other words, Agentic AI will be programmed to perform tasks instead of answering questions. This may not appear to be a big change at first. However, after you begin to use it, it becomes evident.
Today, it can seem like a continuous loop when dealing with AI:
You query → receive an answer → narrow down on your query → query again and so on.
Here, you are also still taking charge of the process in terms of the questions to be asked next and where to proceed.
With Agentic AI, that dynamic begins to change.
In place of a finite number of responses you give a direction or goal and the system proceeds. It does not work flawlessly or entirely on its own, but it tries to get the process going - it determines the next steps, modifies and moves towards the task accomplishment.
Concisely, it transforms AI into not a responsive tool, but a more proactive assistant.
When you have used AI on anything a little bit tricky, be it in planning a campaign, researching a subject, or even planning a trip, you likely have noticed one thing:
AI helps, but you’re still doing a significant part of the work.
You make out the patterns.
It is up to you what you choose to do next.
You continue developing prompts.
This is what Agentic AI is attempting to bridge.
It is not merely about providing smarter responses, but about minimizing the back and forth and letting the system take over part of the process.
And there is a distinction between:
Someone is giving you suggestions
There is someone who is doing the job.
The majority of AI tools in the present day belong to the former. AI is gathering momentum in agentic AI towards the second.
Use a basic case of flight booking:
An old AI system may present you with choices or propose the most appropriate paths. And yet you have to compare prices, monitor changes, check schedules and do the booking yourself.
A system that is agent-driven would however take it another step forward and keep track of changes in prices, make comparisons with options over a period of time and even act on your behalf when the conditions are just the way you want them to be (with your permission).
It is a little change in appearance, however, a significant one in practice.
There is no need to delve into technicalities to grasp this change.
The majority of Agentic AI systems are loop-based:
Goal Action Evaluation Adjustment Repeat.
You used to do all this manually before. The system is now starting to do some of it internally- making decisions, tweaking based on the results and working up to the destination goal.
Even though it is still at its early stages, Agentic AI is already beginning to be implemented in practice:
Customer Support: Systems that seek to address problems not merely in response to a query.
Marketing: The ability to create content not just, but also suggest the next steps to take based on performance.
Operations: Repetitive workflows can be automated with minimal or no input.
It is not faultless outputs that do not require validation, and systems may make errors but the course is evident.
It is often believed that AI is time-saving in a flash.
The reality is that there is still effort being done in using AI:
This can be handled in small tasks. However, when scaled, it is time-consuming.
The attention to agentic AI is growing due to providing something more practical:
suggest inconstancy of involvement not no effort, but more efficient work processes.
This change involves considerations, too:
Control: To what extent should AI be autonomous?
Precision: The slightest errors can lead to bigger implications when processes are automated.
Trust: Individuals are not afraid of AI proposing ideas, but taking them is another step.
Adoption is therefore likely to be gradual due to these reasons.
Not exactly.
Generative AI is concerned with text, image, and code generation.
The agentic AI extends that, whereby those outputs are utilized to perform tasks.
An easy approach to realize it:
This does not imply the replacement of humans; it involves re-spreading the time and energy used.
Reduced emphasis on performance and increased emphasis on decision-making.
It is not simply another trend in the AI space: agentic AI is a significant change in our relations with technology.
Rather than only giving answers, AI is starting to play a more active role in task completion, influencing workflows and alleviating the need to have a human involved at all times. This is not equal to full automation and elimination of human decision-making. Instead, it alters the way work is shared - people are able to put more emphasis on strategy and less on repetitive performance.
That being said, the shift will not occur at once. The issues of control, accuracy and trust are yet to be resolved. Both businesses and users will be interested in achieving the appropriate balance between automation and control.
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