Agentic AI: The Next Evolutionary Stage of Artificial Intelligence
Recent developments in the field of artificial intelligence have enabled systems to evolve from passive tools that merely respond to human commands into structures capable of setting their own goals, planning, and acting independently.
Recent developments in the field of artificial intelligence have enabled systems to evolve from passive tools that merely respond to human commands into structures capable of setting their own goals, planning, and acting independently. This new paradigm, known as Agentic AI, represents a shift beyond reactive behavior — toward systems that can autonomously define objectives, devise plans to achieve them, and manage the execution process step by step. While grounded in large language models (LLMs), Agentic AI distinguishes itself by being able to take action, not just generate responses.
When AI systems first became widely used, they were mostly employed for tasks like text generation or document summarization — unable to act on their own. With the rise of Agentic AI, these systems are no longer limited to responding to commands; they can analyze data independently, conduct research, plan and prioritize tasks, and execute actions in both digital and physical environments through APIs or robotic systems.
These systems can:
Define their own goals and priorities
Create multi-step plans and organize subtasks
Adapt using live data and past experience
Take initiative without human intervention
Integrate with APIs or robotics to execute tasks
For example, in scientific research, Agentic AI can autonomously generate hypotheses, conduct literature reviews, design experiments, and analyze data — emerging as an autonomous AI system that can make its own decisions without constant human guidance.
AI Agent vs. Agentic AI
Although AI Agent and Agentic AI may seem similar, there are key differences.
An AI Agent performs specific tasks efficiently and accurately, primarily automating routine processes.
Agentic AI, on the other hand, can think, plan, and act independently. It empowers AI systems to make decisions and act toward goals autonomously, significantly enhancing performance.
This enables the rapid analysis of complex data sets, the discovery of hidden patterns, faster decision-making, and greater scalability. Moreover, employees and teams can manage complex workflows and projects using natural language alone.
Agentic AI means that AI no longer merely reacts to commands — it can set goals, create plans, and execute them step by step. These systems decompose complex tasks into smaller components, select the necessary tools, leverage real-world data, retain memory of prior experiences, and make independent decisions throughout the process.
While AI Agents execute well-defined tasks (e.g., a customer service chatbot providing shipment details upon request), Agentic AI goes further — setting its own objectives, creating plans, and acting autonomously to achieve them.
In essence, AI Agents act like assistants focused on individual tasks, whereas Agentic AI can manage end-to-end processes. Unlike RPA (Robotic Process Automation), which relies on fixed inputs and predetermined outcomes, Agentic AI dynamically plans its path toward a goal, delegates tasks when necessary, and generates adaptive results throughout the process.
For instance, in a factory maintenance scenario, traditional AI can use pattern recognition to detect faults and locate the cause. Agentic AI, however, can manage the entire repair process — creating maintenance plans, performing quality control, and even ordering necessary parts.
Source: McKinsey & Company, Technology Trends Outlook 2025
Global Agentic AI Trends
Agentic AI is rapidly attracting global attention. Companies are experimenting with small-scale prototypes to explore its potential, though full-scale applications remain limited. However, with increasing investment and interest, adoption is expected to accelerate soon.
According to McKinsey, Agentic AI investments reached $1.1 billion in 2024, accompanied by a 985% increase in related job postings, and a significant surge in patent filings.
As technology advances, closely following Agentic AI’s evolution becomes crucial. With its potential to transform business operations, keeping pace with Agentic AI is more important than ever.
A Gartner study predicts that by 2028, 33% of enterprise software applications will include Agentic AI capabilities — up from less than 1% in 2024. The study also notes that at least 15% of daily business decisions will be made autonomously by Agentic AI systems.
As shown in McKinsey’s report, the impact of Agentic AI varies across domains — limited in operational planning, moderate in R&D, more pronounced in functions like marketing, sales, IT, and HR, and most valuable in procurement and after-sales services.
A concrete example is found in McKinsey’s QuantumBlack Labs project, where Agentic AI workflows were applied to optimize the credit scoring process at a bank. Early results showed a 60% increase in analyst productivity. In this case, an LLM-based agent acted as a manager, creating the work plan and assigning sub-agents to tasks like data analysis, validation, and output generation — demonstrating that Agentic AI is not just theoretical but a practical tool for real business transformation.
Uncertainties and Risks Ahead
Although Agentic AI is rapidly evolving, there are still significant uncertainties today. One of the major risks is that Agentic AI may make incorrect decisions, leading to faulty outputs. The quality of training data, inconsistencies within models, and potential cyberattacks are also considered important risk factors.
Moreover, the impact of Agentic AI on the business world is not limited to technology alone. As the adoption of this technology becomes more widespread, several critical questions arise:
How will the workforce be reshaped?
How will humans and Agentic AI collaborate?
What kind of security and governance mechanisms will be established?
With the automation of routine tasks, will people shift toward more creative domains, or will some jobs disappear entirely?
And finally, to what extent should artificial intelligence operate independently?
All these issues require careful discussion.
Agentic AI represents the next stage in the evolution of artificial intelligence. Unlike simple AI Agents that perform specific tasks, Agentic AI integrates multiple agents, data flows, and orchestration to manage end-to-end business processes holistically. This technology transforms AI from a passive tool that merely supports humans into an active actor capable of making independent decisions and managing processes from start to finish.
Agentic AI holds the potential to drive substantial productivity gains and create entirely new business models. It accelerates decision-making processes, reduces costs, and enhances competitiveness through scalable solutions. By enabling employees and teams to manage complex projects using natural language, it boosts workforce efficiency and overall value creation.
In the startup ecosystem, Agentic AI is seen as a high-growth domain for the coming years — one with the capacity to transform business operations, create new market opportunities, and develop scalable business models.