All You Need to Know About AI Agents VS Agentic AI 

Artificial Intelligence technology is a broad concept of simulated intelligence that is capable of performing tasks that previously required human intelligence. AI is not a one true technology; in fact, it is a set of techniques and technologies working through tools, platforms, and agents. All these technologies fall under the AI technology umbrella and can be called AI-powered. There are 2 important terms in artificial intelligence, AI agents and Agentic AI, which are often confused with each other. Let us understand these terms and a clear AI agents vs agentic AI breakdown.  

Why Should You Know The AI Agents Vs Agentic AI Difference? 

More and more companies are turning towards AI technology to remain competitive in the future market. If you also plan to implement AI in your workflow, you must know basic details about these terms. AI agents vs Agentic AI is not a difference of foundation, but rather a difference in their capacity for autonomy. This is mainly because Agentic AI is an entire AI-based system, while AI agents are the building blocks of this system. In a nutshell, AI agents together build a system called Agentic AI. Both AI agents and Agentic AI are transforming the workflow dynamics, but to implement a comprehensive AI solution for your organization’s needs, it is important to understand both these terms thoroughly.  

What is an AI Agent? 

AI agents are customer-facing or task-focused AI tools trained to perform specific jobs. Their functionality is limited to the specified job role, and they work towards achieving goals. Such agents help automate repetitive tasks or address customer queries in a limited manner. They are pre-trained to perform a certain task, without much flexibility for deviation from that particular behaviour. AI agents can be classified for different tasks as below. 

Types of AI Agents 

Here are different types of AI agents classified based on their function: 

• Learning agents: The AI agents that learn from data set as well as from experience. These agents are like the chatbots that keep getting better with more and more conversation.  

• Utility-Based Agents: These agents are capable of making decisions by calculating possible outcomes, like an AI tradition system for market factor analysis.  

• Goal-based Agents: Goal-based AI agents focus on achieving specific goals. They are like an automated system that focuses solely on task completion.  

• Reflex Agents: These agents only react to inputs based on their training data. It’s more like responding with a price chart when a customer asks for a quotation.  

• Model-based Agents: Model-based agents use the environment as input to make smarter decisions using an internal representation.  

Use Cases of AI Agent 

Let us see the actual application of AI agents in the world to understand them better. You can also get in touch with a reputed AI consulting firm to dive deeper into application-based understanding of artificial intelligence.  

Customer Support: 

The customer support chatbots or virtual assistants we talk to on every other platform are one of the most popular use cases of AI agents. These chatbots can answer questions, provide guidance, and solve common issues raised in the process, without human interference.  

Personal Assistant: 

The one in our phones, personal assistants like Siri, help you set reminders, schedule appointments, and play your favourite music. These tasks are useful but do not require much decision-making, so they can be pre-trained on certain inputs to perform specific functions.  

Email Management: 

AI agents can be utilized to manage your email inbox. Agents can flag important emails, classify your emails, and also provide smart replies based on the context. One can use AI agents to compose emails and reply to emails based on minimal input and in seconds.  

Productivity Tools: 

Productivity tools like Copilots work like an assistant to professionals. For instance, these agents can help write code and debug for software developers.  

What is Agentic AI? 

Agentic AI is an artificial intelligence system that can make autonomous decisions, learn, adapt, take goal-driven actions, and involve reasoning. Unlike AI agents that utilize a single agent to get a task done, Agentic AI utilizes varied and multiple agents to perform tasks, learn from experiences, adapt, and make decisions. Agentic AI is all about autonomy. An agentic AI functions in the following 4 stages.  

• Perception: Gathering data from the world around 

• Reasoning: Understanding what’s going on based on the processed data 

• Action: Deciding what to do in this condition  

• Learning: Improving and adapting over time with feedback and experiences.  

Use Cases of Agentic AI 

Agentic AI is a complete system that uses multiple AI agents to achieve various goals simultaneously. This leads to a comprehensive solution that can learn, reason, process data, and make autonomous decisions. Such Agentic AI solutions are applied in processes that require total autonomy. Let us see the applications of agentic AI to understand the difference better.  

Autonomous Vehicles: 

One of the most interesting use cases of agentic AI is in self-driving vehicles. The agentic AI perceives from the environment on the road, drives accordingly, and learns with every trip, all without human intervention. With every trip and new challenges on the road, it learns new ways to improve driving. The best example is Tesla’s full self-driving system, where agentic AI is utilized for automation.  

Supply Chain Management 

A smart, agentic AI can automate the supply chain management system by analysing orders, predicting demands, stocking up inventory autonomously, and adjusting delivery routes in real-time for efficient operations.  

Cybersecurity 

Agentic AI can detect threats and vulnerabilities by analyzing network activity and responding to potential breaches. Some cybersecurity companies use agentic AI to detect, respond to, and learn from threats in real-time.  

Healthcare 

Agentic AI plays an important role in transforming the healthcare sector. It handles a large amount of patient data, helps with diagnosis through pattern analysis, and helps doctors make more informed decisions.  

Core Distinction Between AI Agent and Agentic AI 

Both AI agents and Agentic AI are extensively used in industry automation. However, to make an informed decision regarding their implementation, one must understand the core differences between them. The points of differentiation in AI agents vs Agentic AI are:

Autonomy and Decision Making 

The most important point of difference between AI agents and agentic AI is autonomy and decision-making. AI agents work within a predefined framework, but can make independent decisions depending on learned patterns and inputs within a specified area. Agentic AI, on the other hand, is capable of more advanced decision-making and reasoning. It includes its proactive approach towards achieving goals and resolving issues, learning from experience, weighing multiple options for complex decision making, and adapting to real-time changes. 

Complexity and Learning 

AI agents can handle simple, predefined tasks, while agentic AI is a step ahead because of its learning, adaptability, and multitasking skills. While AI agents require programming for any updates or changes, Agentic AI can make necessary changes through individual interactions and environmental inputs. 

Functionalities 

Another important difference between AI agents and agentic AI is their functionality. AI agents are programmed to perform specific tasks, while agentic AI can perform multiple tasks that may be achieved in steps and with the coordination of multiple systems or processes. 

Proactiveness 

AI agents react to inputs based on their learnings and training data. Thus, AI agents utilize a reactive approach to function. On the other hand, agentic AI, due to its deep learning and large language processing, can take proactive steps to solve issues in the way of achieving its goals.  

Implementing Artificial Intelligence Solutions 

Understanding AI is crucial if you are planning to implement AI-powered solutions in your workflow. It is important to consult a genuine company that provides comprehensive artificial intelligence development services. AnavClouds Analytics.ai is a one-stop solution for a variety of AI services like Natural Language processing solutions, machine learning services, generative AI development services, and data science development etc. With smooth deployment, years of experience, post-deployment support, and quick response, our AI consulting services are top-notch.  

Conclusion 

The automation of workplaces involves both AI agents and agentic AI, but they are not the same thing. Both of these AI technologies are related because AI agents make the building blocks for agentic AI. Understanding their difference and applications in the real world helps business owners get clarity about which AI solution should be implemented in their organization. AnavClouds Analytics.ai is a reliable AI development companies that can help businesses build an AI-powered foundation for exponential growth in the future. Start your journey today by booking a consultation session with us.  

FAQs 

What is the difference between agentic AI and AI agents? 

The core difference between agentic AI and AI agents is their capability of autonomous decision-making. AI agents are task-specific and thus their deviation from a certain behaviour is also confined within their job role. In the case of agentic AI, they are highly autonomous systems with advanced reasoning capabilities. Another difference is that agentic AI is a multitasker, while AI agents perform one specific task they are trained for.  

What is the difference between GPT and agentic AI? 

GPT focuses mainly on text generation, while agentic AI is an intelligent system made up of multiple autonomous agents that can act, decide, adapt, and learn autonomously.  

What is the main difference between AI models and AI agents? 

The primary difference between AI models and AI agents is that AI models are designed to perform tasks and give insights, while AI agents are built to interact with their environment and perform tasks autonomously.  

Is ChatGPT an agentic AI? 

No, ChatGPT is not an agentic AI. It is an excellent generative AI model, but unlike agentic AI, it can not autonomously make decisions, take actions, or achieve goals.