Agentic AI: What It Is and How It Differs from an AI Agent

 

Artificial intelligence is no longer something futuristic or unfamiliar. It has become a basic tool used across many industries. Today, AI assists with numerous routine tasks by executing our commands precisely. But what if it could act on its own, with little to no human input? That’s exactly what the new generation of technology — agentic AI — makes possible.

 

Unlike traditional generative AI, which strictly follows predefined instructions, agentic AI doesn’t just respond to requests. It can assign tasks to other agents, adapt to new conditions, and find solutions to unusual situations. It is not limited to only what it was told to do.

 

According to Gartner’s report, by 2028 at least 15% of daily work decisions will be made autonomously by agentic systems. It will change the approach to solving tasks in various industries — from logistics optimisation to quality control in manufacturing.

 

But what exactly makes it different from earlier AI systems? How does it achieve this level of functionality? And, most importantly, how can businesses and industries use it to build smarter systems? In this article, we will outline the main principles of this technology and compare it with AI agents.

 

What is agentic AI

The agentic AI definition refers to building AI systems composed of autonomous agents that manage and execute tasks with minimal human involvement. Think of it not as a junior assistant but as a senior colleague who can handle a full project: find the needed resources, assign tasks to other agents, get feedback from them, review it, conduct research, and then, having collected everything, deliver a complete solution to you.

 

An agentic system combines elements of traditional programming, generative AI, and, in some cases, other machine learning models. All of this comes with a new level of proactivity and autonomy. Such systems do not passively wait for your commands like standard chatbots. They have real autonomy: they solve tasks themselves, redistribute them between AI agents, break them into subtasks, and adjust actions if needed, without requiring constant supervision.

 

 

From automation to autonomy: moving from RPA to agent systems

Until recently, companies used RPA (Robotic Process Automation) and chatbots to automate routine tasks. However, these technologies only worked within predefined scenarios.

 

RPA, or robotic process automation, is a technology in which computer programs automate routine business tasks. Robots automate operations that previously required human involvement, but they must be programmed manually. Agentic tools, on the other hand, only need clear instructions and access to a knowledge base to handle unexpected situations independently.

 

Agentic AI vs AI agents

Agentic AI and AI agents are closely related but differ in application and functionality.

 

Agentic AI is an architectural approach to creating complex AI systems that utilise AI agents. These agents can make decisions independently, adapt to environmental changes without constant human control, interact with each other and with other system components (including various ML models) to achieve set goals. Examples include self-driving vehicles, logistics optimisation systems, and customer support systems.

 

An AI agent is a single component within an agentic system. It performs tasks with a certain degree of autonomy within a defined environment. It usually runs on a language model or another AI engine that handles decision-making, planning, and interaction. You can think of it as an employee with a specific job and skillset — and the LLM as their brain. For example, AI agents can power customer support bots, sales assistants, or recruiting tools.

 

Agents perform tasks according to rules, while agentic systems adapt to changes — they can adjust strategies, learn from mistakes, and operate in complex, changing conditions.

 

AI agents vs agentic AI, in short:

 

• AI agents follow predefined rules to complete specific tasks.

 

• Agentic AI adapts, learns from experience, and manages complex, dynamic environments.

 

Criteria	AI agent	Agentic AI
Level of autonomy	Works within fixed scenarios; reacts to inputs. Example: a voice assistant responding to simple queries.	Evaluates context, predicts outcomes, and adjusts strategy. Example: cybersecurity systems detecting threats before they occur.
Task complexity	Handles simple, repetitive tasks such as basic HR queries.	Manages multi-step processes that require coordination, like IT support automation.
Risks and limits	Predictable but limited.	Greater responsibility and data security challenges.
Adaptability and learning	Updates only through manual retraining.	Learns from real-time feedback, improving over time — for instance, coding agents that debug and update their logic automatically.

 

The key difference lies in autonomy. In summary, AI agents are separate components that perform specific tasks, while agentic workflows are a more advanced and organised system where agents interact to achieve higher-quality results.

 

Autonomy and human oversight

Agentic AI makes independent decisions using reinforcement learning (RL). Systems constantly improve their strategies, learning from outcomes to make better decisions next time.

 

Autonomy, however, doesn’t mean lack of control. These systems are designed with built-in safeguards and ethical boundaries in place. They include mechanisms that allow human supervision and intervention when needed.

 

Moreover, agents can decide when to involve a human. A good example is AI-agent chatbots in banking, where users don’t need to call an operator, as the system recognises that a customer’s issue needs human input.

 

Real-world applications

Agentic workflows prove most valuable when adaptability and independence matter.

 

• ​​In finance, agents can continuously monitor market fluctuations, optimise investment strategies, and reduce risks faster than a human could.

 

• In insurance, AI speeds up claim processing, detects fraud, and facilitates automated decision-making.

 

• In customer service, these systems handle routine queries, integrate with knowledge bases for information, CRMs for personalised responses, and ticketing systems to update customers. As a result, first response time (AFRT) can drop sixfold, and operating costs can be reduced 36 times compared to human operators.

 

• In logistics, classic AI can create a delivery schedule, while agentic technology predicts traffic or bad weather, adjusts the route, and ensures timely delivery.

 

• In manufacturing, agents detect inefficiencies and predict equipment failures.

 

• Another example is a cybersecurity AI agent that reveals real vulnerabilities in agents and LLM systems, helping users study them. It simulates attacks to expose vulnerabilities in AI models, acting as a “cyber range” for security testing.
  
  

Sounds like magic, doesn’t it? This is just a small part of the agentic AI examples. For more, see PwC’s study.

What’s next

The agentic technology can change how we work, solve problems, and make decisions. Its potential lies not in replacing human intelligence but in amplifying it and creating new opportunities for collaboration.