Agentic AI. No AI agents. Do you find yourself using these artificial intelligence terms interchangeably? Well, you’ll be glad to know you’re not alone. With all the innovations happening around Artificial Intelligence, keeping track is difficult. 

That’s why we’re here to help clarify the similarities and differences between AI agents and agentic AI, so the next time you hear someone using these terms, you will no longer be puzzled! 

Read on as we explore the meaning, purpose, and use cases of AI agents and agentic AI solutions. We will also present a comparison chart that compares these two emerging technologies side by side. 

What are AI Agents?

Let’s begin with AI agents. AI agents are software applications that work through multi-step problems. They are designed to carry out specific tasks and depend on human prompts to deliver an output. This means they are not autonomous and function only when asked to provide an answer, solve an equation, organize a calendar, summarize a meeting, etc.

AI intelligent agents cannot handle complex workflows. Since they are rules-based, they are only good at automating simple, repetitive tasks and do not possess any inert decision-making capability. AI intelligent agents are virtual helpers that do precisely what you tell them to do without thinking for themselves.

There are different types of AI agents: 

• Utility-based agents evaluate potential outcomes to make the most favorable decision. For instance, in healthcare, these agents can study patient health and assist in diagnosis, treatment planning, and personalized medicine. 

• Goal-based agents are designed to achieve specific objectives and plan sequences of actions to achieve desired outcomes. For instance, goal-based agents can be used in customer service to answer queries and guide users to solutions, making decisions based on the user's input and the conversation context. 

• Reflex agents make decisions based on predefined condition-action rules, responding immediately to prompts. For example, reflex agents can send predefined messages based on specific keywords or sender addresses.

What is Agentic AI?

Unlike AI intelligent agents, which are software applications, agentic AI is a field of artificial intelligence that conducts model research and development. Agentic AI solutions multiple AI agents together to make decisions independently. They are highly autonomous and can handle complex workflows. 

Agentic AI solutions analyze data and situations to improve outcomes and adapt outcomes accordingly. It works like a virtual assistant that can think, reason, and adjust to changing circumstances without human input and direction. Here are some examples: 

• In website creation, agentic AI solutions can utilize multiple AI intelligent agents to carry out the entire task from start to finish, from researching content and designing the website to coding, implementing, testing, and optimizing the website for SEO. 

• In supply chain management, Agentic AI can orchestrate multiple tasks, such as checking available suppliers, confirming prices, reconfiguring distribution routes, and finding additional vendors to meet higher demand.  

• In driverless cars, agentic AI solutions can analyze real-time data from cameras, sensors, and traffic conditions to avoid obstacles, optimize speed, and make critical driving decisions.

Agentic AI vs. AI Agents Comparison Chart

Rules-based AI agents strictly follow predefined conditions, lacking flexibility beyond their programmed constraints. Goal-based agentic AI solutions, on the other hand, adapt dynamically to real-time data, using contextual understanding and autonomous reasoning to navigate complex scenarios. 

In comparison, both differ in many ways: 

In Summary

AI agents and agentic AI solutions are closely related concepts commonly used across many applications today. If you plan to integrate either of the two within your business, it is essential to understand the similarities and differences. 

While AI agents depend on human prompts and direction to answer questions or solve problems, agentic AI solutions act autonomously, learning and adapting as the environment changes. Rules-based AI solutions have limited decision-making capability, while goal-based agentic AI solutions can make decisions to accomplish their goals. 

Choosing between AI agents and agentic AI is never a question of this or that. Based on your unique use case, you can select the right approach to automate tasks, address concerns, and achieve a specific goal. 

A clear understanding of the differences is essential for anyone wanting to succeed in the AI era.