When were you first introduced to the terms AI Agents and Agentic AI? Most likely, it was last year. The two terms might seem interchangeable, but they’re quite different.

AI Agents are good for handling specific tasks. They follow rules, use tools, and apply reasoning to get things done. On the other hand, Agentic AI has multiple agents working together autonomously, adapting to challenges, and tackling much more complex tasks. In this blog, I’ll break down the differences, use cases, and challenges based on this research paper.

Table of contents

• What are AI Agents??
• Why Move from Agents to Agentic AI?
• Example of AI Agent vs Agentic AI
• AI Agent vs Agentic AI
• AI Agent Application
• Applications of Agentic AI
• Limitations AI Agents
• Limitations of Agentic AI
• Conclusion

What are AI Agents?

AI Agents are computer assistants that are meant to perform specific tasks. They are based on large language models (LLMs) or vision models. They operate based on a given set of instructions and sometimes require external tools. But they usually work within a limited boundary. They’re not designed for tackling wide problems but are great at repetitive, goal-oriented tasks such as filtering emails, summarizing reports, or retrieving data.

Read our article on different types of AI Agents to learn more about this concept.

Why Move from Agents to Agentic AI?

AI Agents work well but have their limitations. They’re fine for answering customer questions or doing routine tasks, but they’re not useful when the situation gets complicated. They can’t multitask or accommodate shifting conditions.

This is where Agentic AI comes in.

With several specialized agents acting together, Agentic AI can handle intricate workflows. These agents talk to each other, divide tasks, and make decisions together. And with persistent memory, they can learn and make better decisions over time. Coordination between agents makes things go smoothly, even when they encounter surprise obstacles.

Also Read:

• Agentic Frameworks for Generative AI Applications
• Top 4 Agentic AI Design Patterns

Example of AI Agent vs Agentic AI

Let’s take a simple example. Consider a smart thermostat as an AI Agent. According to your preferences, it maintains the room temperature perfect. As time passes, it understands your routine and assists in saving energy. But it does not integrate with other devices or change according to factors such as weather or energy prices. Even though it does its job perfectly, it does it independently.

How can Agentic AI address this issue?

Agentic AI can be like a whole smart home ecosystem. Multiple agents (weather forecasters, energy managers, security monitors) work together. A weather agent detects a heatwave and informs the energy agent to pre-cool the house. Meanwhile, a security agent activates the surveillance cameras when you’re not home. These agents interact with each other in real time, ensuring your home is comfortable, safe, and energy-efficient.

Much more powerful, right?

Must Read: How to Become an Agentic AI Expert in 2025?

AI Agent vs Agentic AI

Now, let’s dive into the specifics of how these two terms differ across various factors like function, architecture, and coordination. We’ll also look at their respective strengths and challenges. Here’s a breakdown:

• Scope and Complexity: AI Agents are great for specific, defined tasks, but Agentic AI handles more complex, multi-faceted goals.
• Core Purpose: AI Agents have a single task to perform, whereas Agentic AI streamlines complicated processes with several agents cooperating.
• Components of Architecture: AI Agents are founded on LLMs, whereas Agentic AI has several LLMs and typically incorporates different systems. It also entails several agents cooperating with each other, whereas AI Agents usually operate independently.
• Operational Process: AI Agents operate by invoking tools for task execution, whereas Agentic AI uses inter-agent interaction and coordination over multiple steps.

Taxonomy Summary of AI Agent Paradigms

Core Function and Goal

Architectural Components

Operational Mechanism

Scope and Complexity

Interaction and Autonomy

AI Agent Application

Let’s look at a few usecases of AI agents:

Automation of Customer Support

AI agents are simplifying customer support and internal search. For support, they respond to questions such as “Where is my order? ” by extracting information from company systems and responding in seconds. They can also monitor orders or initiate returns. Within the organization, workers leverage the same AI to locate such things as meeting minutes or policy changes. Just ask a question, and it provides a concise, direct answer with citations. Itsaves time,decreasessupportrequests, andenablesteamstoworkmorequicklyandintelligently.

Personalized Content Recommendation

AI agents assist in making content personal and accessible. On sites such as Amazon or Spotify, they discover what people like by observing clicks, searches, and purchases. From this, they suggest products, videos, or songs that are similar to your interests—such as recommending gardening books after purchasing tools. In businesses, AI agents in products such as Power BI Copilot enable anyone to pose questions such as “Compare Q3 and Q4 sales” in natural language. The AI then converts that into a chart or report without the assistance of a data analyst. This increases engagement for the users and speeds up and simplifies data reporting for teams.

Applications of Agentic AI

Let us consider a few usecases of Agentic AI:

Collaborative Medical Decision Support

In hospitals, various agents perform various tasks: one reviews patient history, one watches vitals, and a third recommends treatment. They collaborate, exchanging information and ensuring the advice is reliable and consistent. For instance, in an ICU, one agent recognizes early indications of sepsis, one gets recent surgeries, and one offers recommendations based on medical guidelines. Physicians review and validate the final plan. Thiscollaborationlightenstheloadonphysicians,acceleratesdecision-making, andenhancespatient care inriskysettingssuchasICUs and cancerunits.

Intelligent Robotics Coordination

In orchards or warehouses, various robots play various roles, some harvest fruits, others create maps or transport loads. A master AI, referred to as an orchestrator, ensures they seamlessly collaborate. For instance, in an apple farm, drones survey trees and locate ripe fruit. Picker robots are dispatched to the optimum locations, and transport bots shuttle crates around according to real-time requirements. When one robot fails, others compensate automatically. This arrangement enhances productivity, reduces labor expenses, and responds to unexpected shifts more effectively than traditional fixed-program robots.

Limitations AI Agents

ThoughAI Agents areproductive, theyhavesomeimportantlimitations:

• Short-Term Focus: AI Agentsarepooratlong-term planning andflexibility,andthusare not well-suited foractivitiesneedingfrequentadjustments.
• CausalMisunderstanding: Theytendtoconfusecorrelationwithcausation, which cangeneratemisleadingconclusion.
• InheritedConstraintsfrom LLMs:BecauseAI AgentsrelyonLLMs, theyriskinheritingbiases,beinginput-data-sensitive, andbearinghigh operationalexpenses.

Limitations of Agentic AI

Agentic AI, though more capable, isn’t without challenges of its own:

• Increased Complexity: Since there are several agents acting simultaneously, causes become harder to identify and predict outcomes.
• Coordination Issues: The interaction between agents can at times lead to delays or errors.
• Scalability: As Agentic AI systems increase, they become more difficult to scale and debug, with problems that are difficult to fix.
• Security and Ethics: The more agents there are, the higher the risk for security violations and ethical issues. Keeping these systems in line with appropriate regulations grows more difficult as they scale.
• Emergent Behavior: As agents communicate more frequently, their behavior becomes more random, making it more difficult to contain or forecast outcomes.

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Conclusion

AI Agents and Agentic AI are both powerful tools, but they serve different purposes. AI Agents are perfect for single, well-defined tasks, while Agentic AI takes things to the next level, managing complex workflows with multiple agents. However, both face challenges, especially when it comes to coordination and scalability. By understanding these differences, we can apply the right tool for the job as these technologies continue to evolve.

So next time someone mixes them up, you’ll know how to set it straight!

All the images and tables used in the article are taken from this research paper.

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