AI agents vs agentic AI: understanding the evolution of intelligent systems

You may have heard the terms agentic AI and AI agent frequently of late, and given the similarity in their names, they may seem to be the same thing. However, despite sharing a similar name, the two serve entirely different purposes.

That’s why, based on the research I’ve conducted along with the Cybernews research team, I aim to clarify the difference between AI agents and agentic AI. I’ll note the differences in their purpose, their different uses, real-world examples, and how you can get the most out of each type of AI.

AI agents vs agentic AI: key differences

To help you visualize the difference between AI agents and agentic AI, I decided to make a table explaining the differences between the two:

	AI agents	Agentic AI
Role	Executes a specific task or a narrow set of tasks	Owns an outcome and coordinates many tasks to achieve it
Composition	Typically, a single agent or simple loop calling tools	A system of multiple agents/tools plus a planning/orchestration layer
Complexity	Handles mostly simple or very specialized tasks	Can handle a variety of tasks, including complex subtasks
Decision-making	Reactive	Proactive
Token usage	Lower	Higher
Use cases	Simple tasks, e.g., customer support, coding assistance	Advanced tasks
Response time	Lower	Higher
Team analogy	Individual specialists doing given tasks	Manager coordinating multiple specialists

What are AI agents?

An AI agent is a term used to describe AI software that’s designed to perform specific tasks within defined parameters. These tasks can include simple coding, customer support, or data analysis.

AI agents primarily operate based on a straightforward framework. These rules define the AI/machine learning models the agent can utilise, the permissions it receives within its systems, and the boundaries it cannot exceed.

Once an AI agent’s basic parameters are set, it waits for user input. These can be simple commands, data to analyze, or, for example, user tickets. Next, it takes those inputs to perform its task within the preset boundaries.

This means that AI agents have a wide range of applications. It can be as simple as a proofreading agent for articles, or something as complex as a robust data analysis machine. These characteristics make AI agents very effective when you need to create a specialized LLM that will focus all its resources on performing a given task.

Components of AI agents

AI agents are composed of several modules that enable them to operate and perform various tasks. These include:

• Input. Responsible for receiving data or user requests, essentially, the eyes and ears of an AI agent.
• Logic. Responsible for creating the underlying logic for the agent. This is often an LLM model that the agent operates within. You can think of it as the AI agent’s brain.
• Execution. This module is responsible for performing the tasks, e.g., fetching data, coding, calling APIs, and responding to users. Think of it as the AI agent’s hands.

Additionally, AI agents are often fitted with two additional optional modules:

• Memory (context-tracking). This allows agents to retain prior states and use them to inform future decisions. This can be particularly useful in data analysis or in customer interactions.
• Integrations. AI agents can be easily integrated with other tools and products like external APIs. This makes it easy for developers to insert them into their workflows. For example, these can be plugged into a hosting service to help with coding.

What is agentic AI?

Agentic AI is essentially an advanced AI setup. It’s an advanced, autonomous system capable of answering user requests, as well as pursuing goals and planning multi-step workflows. Agentic AI can essentially coordinate the building of complex projects without the need for detailed human prompting.

This means that agentic AI is far more capable than an agent and can create a whole string of decisions based on a single prompt. For example, if I instruct it to create a functional Tetris clone, agentic AI will be able to quickly set up a plan for execution. It will then research the rules of Tetris, start coding, test the code, and finalise the setup before providing me with a final output.

Agentic AI is thus much better for tasks that require complex reasoning, multitasking, and planning. It can independently turn a simple prompt into a very complex set of tasks. In a way, you can consider agentic AI to be a collection of AI agents capable of memorizing, planning, and orchestrating the performance of tasks.

Examples of agentic AI in action

A recent example of agentic AI being put at the forefront is Google’s Antigravity coding interface. There, you’ll find an agentic AI capable of turning a simple prompt into a series of tasks for various AI agents.

When you prompt Google Antigravity to create an app, it will start by analyzing the user’s request and figuring out the best way to complete it while giving you updates every step of the way. It also checks its work to ensure that it’s performing in accordance with its guidelines. These tasks are actually sometimes performed by AI agents, which agentic AI can prompt far more efficiently than humans.

You can also experience Agentic AI when using any chatbot’s deep research mode. Here, the AI creates a comprehensive research plan and utilizes it to reason in a far more in-depth manner than a typical search agent. This eliminates the need for human input during the search, giving a comprehensive report based on multiple sources and advanced reasoning.

AI agents vs agentic AI: workflow

To further illustrate the difference between the two types of AI, I examined how their workflows differ from one another. Let’s start with AI agents.

An AI agent's workflow is rather simple. It’s comprised of the following steps:

1. Input. A question is being asked in a support chat.
2. Task execution. Analyzing the question and finding an answer.
3. Output. Answering the question in a live chat.

Agentic AI, meanwhile, has a far more complex process. Here’s how it looks:

1. Input. This can be either a user’s prompt or a system prompt based on prior coding.
2. Creating sub-tasks. The agentic AI then analyzes the task and creates sub-tasks to execute it.
3. (Optional) dispatching sub-agents or tools. Agentic AIs can then prompt other models or tools to perform specific tasks.
4. Execution. The model then executes the tasks it set out, adapting along the way by testing the planned output to see if it suits the original prompt's requirements. It can also use memory or context retention to execute its task.
5. Output. The agentic AI then outputs the results of its work.

As you can see, agentic AI is far more complex. Its ability to create a complex workflow and execute multi-step tasks based on a single prompt is a big difference from regular AI reasoning. However, note that this means that agentic AIs use more resources and take more time to produce results.

Characteristics comparison

So, how exactly do these two approaches to AI work? Where can you use them, and how can you make them work for your benefit? Here’s a breakdown:

Dimension	AI agents	Agentic AI
Primary role	Executing specific tasks within defined parameters	Pursue broader goals and outcomes by planning and coordinating tasks and tools
Scope of work	Narrow and task-focused (e.g., answer a ticket, run a query, schedule a meeting)	Wide, multi-step workflows spanning multiple tasks, systems, or domains
Autonomy level	Lower autonomy, only inside a tightly defined domain and rules	Higher autonomy, chooses strategies, decomposes goals, and revises plans
Use of tools/LLMs	May call tools or an LLM to complete its one task	Chooses which agents/tools/LLMs to invoke, in what order, and with what prompts
Memory and learning	Often stateless or with local, short-term memory	Often stateful with shared, longer-term memory across tasks and sessions
Examples	Zendesk AI, GitHub Copilot Chat, Google Calendar scheduling assistant	Google Antigravity, Windsurf, marketing automation copilots (e.g., HubSpot)

Real use cases

With each AI approach having different pros and cons, the two have very different use cases. Let’s take a look at the potential uses for both.

AI agents, given their simplicity, have several uses when you need quick and reliable responses for non-complex prompts. Here are some examples of potential use cases:

• Customer support agents. When dealing with customer support, speed is paramount, making it a perfect choice for AI agents. Additionally, restricting an AI agent's abilities will be helpful in case a user gets too creative with their prompts.
• Scheduling assistants. You don’t need an entire reasoning system to schedule your tasks. An AI agent is perfectly suited for the role of a low-resource assistant that can simply add things to your calendar.
• Simple data lookup. If you have data that requires parsing, an AI agent will get it quicker and with less hassle than an agentic AI would.
• Coding assistant. While Agentic AIs are better for building entire apps, if you need a model to check your code or help you write simple elements of code, an AI agent will be much quicker.

Agentic AIs, on the other hand, are more useful for more complex tasks. These include:

• Research. Agentic AI is excellent at handling complex research tasks, building complex research plans that allow it to dive deeper into topics.
• Advanced app building. Agentic AI is excellent at building complex workflows. That’s why it’s used by Google’s Antigravity IDE, where an agentic AI takes top-to-bottom control of coding tasks.
• Project coordination. An agentic AI’s planning capabilities are excellent for project management and coordination. With the right setup, it can build workflows and analyze their efficiency.
• Dynamic data analysis. If you need analysis and insights beyond simply parsing data, agentic AI can use its ability to orchestrate the building of an entire data analysis workflow, from data extraction to a thorough analysis.

Why the difference matters

Choosing the right AI paradigm is crucial for efficiently using the tool. If you use an agentic AI for a simple task, you may lose time and money due to its process. On the other hand, using an AI agent for a more complex task may result in unsatisfactory output, leading to losses and frustration.

That’s why you shouldn’t rely on buzzwords and marketing claims and instead consider your needs. Here’s what you should think about when picking an AI paradigm:

• Task complexity. AI agents are better suited for simple tasks, while agentic AI is better for more complicated operations.
• Task predictability. If you have a lot of unpredictable tasks in your workload, an agentic AI will be far better equipped to handle the changes than an AI agent.
• Security. AI agents are far easier to secure from misuse and require far less access from your end. Agentic AI, meanwhile, requires wider access, which can prompt misuse.
• Budget. AI agents are far cheaper to operate than agentic AI, with lower token consumption due to their limited scope.
• Speed. AI agents respond much faster than agentic AI. This makes agentic AI a poor choice for fields that require fast responses.

Thinking about these factors will help you decide between the two approaches and create a setup that suits your needs. The two can be used separately or in combination, but the most important aspect of using them is using them with your goals in mind.

Which one should you choose?

Your choice between AI agents and agentic AI should always depend on your specific needs. When you have a predictable, low complexity task load, an AI agent will do as good a job as an agentic AI, while consuming far fewer resources.

However, if you have a task that has multiple steps or requires a deeper understanding of complex topics, an agentic AI will be able to adapt to it far better than an AI agent, using its skills to give you advanced outputs. What’s more, in some projects, these AIs may work in tandem, with agentic AIs orchestrating AI agents to perform specialized tasks.

If you need a real-life comparison, think of it like this: you wouldn’t hire a chemistry professor to perform simple lab work, and you wouldn’t hire a beginner lab technician to design and oversee complex experiments. Depending on your goals, you may hire one of them or both. However, the most important thing is to match their skill set to their tasks. Picking between agentic AI and AI agents is essentially just that.

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