Generative AI vs. Agentic AI: What Is the Difference?

As artificial intelligence (AI) continues to evolve, so does the language used to describe it, leading to more specialized terms like generative AI and agentic AI. While “AI” was once a catch-all term for computer systems mimicking human reasoning, the field of AI has expanded into a spectrum of specialized approaches. Learning about newer types of AI and how to distinguish between them can help you better grasp the field and uncover new opportunities in your personal, professional, and educational journey.

What is the difference between generative AI and agentic AI?

Generally, generative AI creates content based on training data, while agentic AI performs actions independently. You can remember this by thinking about how generative AI “generates,” while agentic AI essentially acts as its own “agent.”

What this means, at a high level, is that generative AI is a system that acts as a digital creator, producing media content such as text essays, computer code, music compositions, image designs, and more. Generative AI responds specifically to your instructions and requests and won’t take action on its own. 

For example, you might prompt a generative AI algorithm to “output a picture of a cat sitting in a tree in summer,” and it will do so if the algorithm is working correctly. If you look at the picture and think, “The sunset should be more vibrant” or “I want the cat to be a different color,” you can instruct the algorithm to update the output. Without your input, the algorithm won’t output anything further or make updates.

Agentic AI, on the other hand, performs tasks autonomously based on predefined rules or constraints. Rather than just generating content, this type of AI can make decisions, take actions, and coordinate multistep actions without ongoing prompting or inputs. This system acts more as a digital assistant than a creator. Instead of just generating an email, agentic systems can create the email, send it, schedule a meeting in response, and otherwise manage ongoing projects.

What is generative AI?

Generative AI refers to a type of deep learning model that takes training data, or “raw” data, and learns to create outputs that are statistically likely based on what it’s deduced from existing data. Professionals originally used this with numerical data, but it has since expanded to images, audio, video, and text data types. 

At its core, generative AI uses deep learning models, such as large language models (LLMs), to understand and mimic the structure of its training data. This allows it to produce outputs that are novel yet rooted in their learned patterns. For example, if you trained your generative model on medical notes, you could use it to draft clinical summaries or create medical notes based on new patient data.

What is the difference between generative AI and AI?

Generative AI is a subset of artificial intelligence. Traditional AI is broader, including systems that classify data, predict outcomes, and recognize patterns. Classic examples of traditional AI include determining whether an email is spam or not, or determining whether an X-ray shows signs of an injury (yes/no). Generative AI creates and outputs information based on training data. It might go beyond determining whether an email is spam to create a response email, or it might generate a detailed description of the X-ray finding.

What can you use generative AI for?

Generative AI is incredibly versatile, meaning you can use it for many different purposes around content creation. In your field, consider what type of outputs you create (e.g., promotional films, emails, reports, data summaries) and whether generative AI could help you streamline some of these tasks. For inspiration, consider the following examples across fields.

Human resources 

In human resources, you can use generative AI to create more value and engagement throughout the employee lifecycle. You might use it to generate better-written job descriptions, create performance reports, summarize employee feedback, create tailored employee development plans, and create chatbots to answer common questions. 

Health care

You can use generative AI in health care beyond summarizing medical notes. You can use generative AI to create financial plans and summaries, deliver health care information more effectively, create risk mitigation strategies, and even personalize medical treatment plan suggestions. 

Education

Within education, generative AI has tremendous power to personalize the learning system and create novel materials that help students learn more effectively. This could involve creating materials based on the topics of current syllabi but can extend to updating learning plans, identifying and creating interdisciplinary materials, converting the delivery format of resources, and creating interactive learning sessions. 

What is an example of generative AI?

One of the examples of generative AI that has picked up the most press is ChatGPT. ChatGPT was developed by OpenAI and is capable of generating human-like responses in real-time based on the context of user inputs. 

Users have explored types of applications across both personal and professional areas of their lives. For example, you can ask ChatGPT to create a workout plan based on your current fitness level, schedule, resources, and preferred activities. The algorithms will then output an example fitness schedule, including exercises, repetitions, equipment needed, and so on. You could also use ChatGPT to brainstorm ideas, generate computer code, draft email responses, give advice or feedback on current manuscripts, and so on.

Generative AI pros and cons

While generative AI can accelerate content creation, it’s not without fault. In some cases, generative AI may create inaccurate or fabricated content, which can be detrimental if you aren’t fact-checking the information provided. It could also replicate bias in the training data, depending on the type and volume of training information you had available. To create a more accurate model, you typically need a high amount of data and adequate computing power, which can be a constraint in some cases. 

On the other hand, when used carefully, generative AI is able to help you reduce time spent on repetitive tasks and can help you come out of a “creativity rut” when it comes to brainstorming new topics or outlines. You can also use it to help build or refine business ideas, such as identifying a brand voice or ensuring your copy is in line with the message you are trying to convey. Ultimately, generative AI is a powerful tool to help you enhance your workflow and outputs, but it isn’t a replacement for human cognition and creativity. 

What is agentic AI?

Agentic AI goes beyond generative AI to operate autonomously toward a defined goal. This means that agentic AI systems not only can generate content and complete predefined tasks, but they can also take initiative, adapt to changes, and carry out workflows. Agentic AI uses a four-step process:

1. Perceiving the data and extracting meaningful information

2. Reasoning to understand the tasks and generate potential solutions

3. Acting based on available tools to execute the determined plan

4. Learning based on feedback to improve plans in the future

The key to the autonomy of the agentic AI system is that it understands the overarching goal, which could be anything from “plan my summer vacation” to “help the user decide on the appropriate product.” The system uses LLMs in combination with cognitive modules to communicate using natural language while reasoning using more complex algorithms. From this understanding, the system can create sub-tasks and execute each step in a logical sequence. This offers personalized support that tailors to each situation. For example, if you were wondering whether it makes sense to take out a particular loan, an agentic AI system could check your accounts, assess risk, create a plan for how you were going to pay off the loan over time, and recommend a game plan based on your priorities.

What can you use agentic AI for?

When you have goals that require multiple steps and decision-making, agentic AI can help you figure out and take the steps to get there. This makes it ideal for more complex or time-consuming workflows. Agentic AI is still in the early stages of development, but a few areas that are likely to be early adopters of this type of technology include the following. 

Research assistance

Agentic AI can help you synthesize findings, track down supporting evidence, and adapt your search strategy based on what it learns. It can also help determine the feasibility of different approaches and operate lab equipment to run tests, providing more information on the potential benefits of different approaches. 

Project planning

If you’ve ever planned a vacation with others, you know that it often involves a series of phone calls, searches, bookings, and coordination across different timelines. Agentic AI has the capability to break down itineraries, determine effective ways to stack events, make bookings autonomously, and communicate the information systematically. 

Software development 

As a developer, you can use agentic AI algorithms to offload routine tasks. While you focus on higher-level logic and ideas, agentic AI can write and debug code, update related documentation, and even deploy projects to meet your end goals without needing your input at each step. 

Example of agentic AI

An exciting example of agentic AI is ChemCrow, which is a chemistry-focused AI agent able to plan and execute complex chemistry tasks [1]. This system was designed to go beyond LLMs by integrating chemistry-specific tools into the model such as Name2SMILES (used for molecular analysis), ModifyMol (to modify a specific molecule), and PatentCheck (used to check for existing patents on a molecule), among several others. This allows ChemCrow to autonomously assist in drug discovery and support the discovery and design of novel materials. 

ChemCrow uses the “thought, Action, Action Input, and Observation format,” which is a thought and acting format, allowing it to reason through each task using its toolset. Researchers have been experimenting with ChemCrow's capabilities, and so far, it’s shown the ability to synthesize insect repellent and aid in the discovery of novel chromophores without direct human oversight. 

What is the difference between traditional AI and agentic AI?

Traditional AI relies on user inputs and training data to guide exactly what should be done, while agentic AI operates more autonomously. For example, your traditional AI algorithm may learn to classify dogs in an image based on labeled training data. Agentic AI, on the other hand, operates more independently to accomplish a goal without needing step-by-step instructions. Instead of just classifying a dog image, it could organize a photo album, detect all dog images, categorize by breed, and label the album without specific user inputs. 

Agentic AI pros and cons

Agentic AI is great for experimentation and workflow offloading, making it an effective way to save time, increase productivity, and utilize AI for complex problem-solving. The learning module with agentic AI systems enables continual feedback and adaptation of the algorithm, allowing the performance to increase over time. This cognitive ability also allows for multi-agent systems to emerge, which is when multiple agentic AI systems work together. Using this, you could use agentic AI to coordinate your workflow with your colleagues’ workflows, such as planning meetings or events across several calendars, or you could also use it to synthesize medical information from several specialists to create a more comprehensive diagnostic plan for a patient.

That being said, agentic AI is a relatively new type of system, and it comes with risks. Because agentic AI systems are highly autonomous, they have the ability to make decisions that are difficult to audit or explain, and sometimes, they may even produce false information. It’s vital to ensure safety, transparency, and ethical use when deploying this type of system. Systems in play, such as ChemCrow, have built-in safety features such as ExplosiveCheck (check for explosive molecules) and SafetySummary (generate a safety overview of each molecule) so that overseeing scientists are aware of potential safety hazards. 

Generative AI vs. agentic AI

While you can use generative AI and agentic AI to produce content, the main difference is in their autonomy and intent. Overall, consider the following overview.

Learn more about artificial intelligence on Coursera

Generative AI relies on prompts to produce outputs, while agentic AI can make decisions and take several actions by itself to meet a purpose. On Coursera, you can take a range of courses, Specializations, and Professional Certificates catered to different AI topics. You can find options designed for your background, including the Generate AI for Everyone course, where you can explore what generative AI is, how it works, common use cases, and what this technology can and cannot do.