Agentic AI vs. Generative AI:
Key Differences and Business Benefits

“How is agentic AI different from generative AI? Aren’t they the same thing?” you may ask. Not exactly. While both use LLMs to generate output, generative AI and agentic AI are used to reach different goals.

That is why we wanted to write this article: to clarify the difference between agentic AI and generative AI, showcase their advantages and use cases, and help you choose the right AI solution. Understanding the difference between generative AI and agentic AI isn’t hard, but it’s important if you’re planning to use or develop an AI solution for your business. So, let’s take it from the top.

Generative AI is a whole class of artificial intelligence designed to create new artifacts. It can’t make predictions or classify data, but its generation capabilities are unparalleled. Based on the patterns it has learned, generative AI can produce original text, video, audio, images, code, and more. It can even create synthetic data on which AI models are then trained.

All this is thanks to large language models (LLMs) trained on enormous volumes of data to generate coherent content that can pass for human-created work. This includes:

• Writing blog posts, product descriptions, and social media copy
• Creating logos, product mockups, and digital artwork
• Auto-generating code snippets, unit tests, or documentation
• Summarizing customer conversations, drafting email replies, or powering chatbots

According to Gartner, businesses keep seeing generative AI as an opportunity. In fact, almost two-thirds of companies surveyed in 2024 are using GenAI across multiple business units—a 19% jump from 2023. And the business functions that have adopted generative AI, or are planning to, are unsurprisingly:

• Customer service
• Marketing
• Sales

All three of these benefit from the outputs of generative AI. However, generative AI doesn’t really understand your goals, can’t manage tasks, or autonomously interact with systems—for that you need agentic AI.

Agentic AI allows systems (often in the form of AI agents) to work autonomously and act on behalf of a user or system to accomplish goals.

Agentic systems aren’t a new discovery, but previously the process required loads of rule-based programming or training very specific models. Now, thanks to generative AI, building AI agents has become much easier.

Taking content generation to the next level, AI agents are particularly useful for businesses, as they can:

• Handle unpredictable scenarios and use cases
• Reason and decide which steps to take to complete a task
• Understand human language
• Be operated by non-technical employees
• Integrate with and operate other software

Agentic AI fuels automation at the decision-making level, so it’s especially helpful in domains where multi-step processes or dynamic environments make traditional automation too rigid or expensive.

Full disclosure: agentic AI vs. generative AI is not a thing because they aren’t rivals. In fact, agentic AI systems use generative models as their “brains” for natural language understanding. AI agents are actually called generative AI agents, and McKinsey describes them as “the next frontier of generative AI.”

That being said, they’re not the same. While agentic AI still basically generates its input, it doesn’t just respond to prompts—it plans, reasons, executes multi-step tasks, and adapts to feedback. This highlights the differences between the business purposes and tasks that generative AI and agentic AI can take on.

Purpose and Functionality

Generative AI is built to generate artifacts (text, images, videos, code) based on the patterns learned from large datasets. Basically, it tries to predict the most likely next piece of information (word, pixel, data point) and do this in the most human-like form. It’s good for improving creative processes, automating simple, repetitive tasks, and summarizing information.

Agentic AI is built to take action with a specific goal in mind. AI agents can plan, decide, and execute whole sequences of actions with little or no human oversight. This makes them more like digital assistants or even teammates that automate workflows and free you up from repetitive decision-making. Speaking of which…

Decision-Making and Autonomy

Generative AI doesn’t make decisions, but it’s pretty good at interpreting various forms of input (written, oral, in different languages, with mistakes, etc.) and generating a matching output. It may seem like generative AI is reasoning when answering questions or offering options, but that’s just an illusion since it can only give you what it has learned from the training datasets.

Agentic AI has a much higher degree of autonomy: it can evaluate its own results, retry actions, or course-correct based on outcomes. If you give an AI agent a goal, it will decide each future step on its own, adjusting the plan and even calling on external tools when needed.

Business Applications

Generative AI is already transforming creative, customer-facing, and technical positions. Businesses use it to produce personalized marketing materials, product descriptions, images for campaigns, assist in code generation, and improve chatbot responses.

Agentic AI is used to power smart assistants, monitor systems, and take action. For instance, it’s a great fit for ops teams managing alerts, monitoring systems, and triggering actions, or AI R&D teams building custom agents to perform complex tasks.

Limitations and Challenges

Generative AI is notorious for “hallucinations”—incorrect or biased outputs. Additionally, the output quality relies heavily on the prompt (how detailed it is) and the data the model has been trained on. Plus, generative AI may struggle with tasks that require memory and long-term context.

Agentic AI systems are much more complicated to build and deploy. Plus, because the agents act autonomously, there’s a higher bar for reliability, explainability, and system integration. For businesses, this means agentic AI often requires custom development.

If we had to answer the question “What is the difference between generative AI and agentic AI?” with one word, it would be “application.” To demonstrate this, we’ve gathered real-life examples of enterprises using agentic AI and generative AI in the same industries.

E-commerce

Generative AI: Belk

Belk uses generative AI powered by Google Cloud to automate 90% of its product description writing. This creates a consistent and unified brand voice across its vast e-commerce catalog. Generative AI has helped Belk maintain high-quality, on-brand messaging while freeing up staff to focus on more strategic, creative work.

Agentic AI: Shopify

Shopify’s AI agent, Sidekick, helps merchants manage their stores by handling everyday tasks through simple text commands. It can answer questions about sales, update product listings, create discounts, and more. It works through text commands directly in the Shopify interface, making store management easier, especially for merchants without large teams.

Finance

Generative AI: Morgan Stanley

Morgan Stanley built a custom generative AI assistant based on OpenAI’s models to help its wealth managers access and summarize internal research quickly. Advisers can ask questions in plain language and get relevant insights to make informed decisions.

Agentic AI: Bud Financial

Bud Financial uses agentic AI to automate financial tasks for consumers, like moving money between accounts to avoid overdraft fees or earn more interest. The AI relies on each customer’s financial history to make these decisions. Trials show it could have saved low-income users hundreds in fees while generating over $500 in annual profit for 27% of users at one US bank.

Customer Support / Customer Experience

Generative AI: Klarna

Klarna replaced a large part of its customer service team’s workload with an AI assistant built on OpenAI’s GPT-4. It handles two-thirds of support inquiries, provides 24/7 availability, and has led to a 25% reduction in repeat inquiries.

Agentic AI: Lenovo

Lenovo is using AI agents to transform customer support by handling a large share of interactions (chat, voice, email, etc.) without needing a human. These generative AI agents now resolve up to 70–80% of customer issues, cutting call handling time and boosting productivity. Instead of just assisting, they act like reliable digital deputies, taking care of repetitive tasks so human teams can focus on more meaningful work.

Healthcare

Generative AI: Mayo Clinic

Mayo Clinic is testing Google’s Med-PaLM 2 to help with summarizing medical records, drafting communications, and retrieving clinical information. The goal is to cut down on time spent on documentation and give staff easier access to the data they need.

Agentic AI: Hippocratic AI

Hippocratic AI is building virtual agents that take on follow-up care tasks—such as checking in with patients, providing medication reminders, and flagging concerns to staff. These agents are designed to work independently and support care teams by taking over routine communication.

There’s no one-size-fits-all answer—both agentic AI and generative AI have distinct strengths, and the best choice depends on your specific use case, industry, and goals.

Generative AI is ideal when the task involves creating content (writing product descriptions, summarizing documents, generating emails, drafting code, etc.) because it saves time and improves consistency. It’s already transforming areas like marketing, customer service, and healthcare documentation.

Agentic AI, on the other hand, is better suited for tasks that require autonomy, multi-step reasoning, and interaction with systems or tools. It’s especially useful for operations, customer experience, and finance—anywhere AI needs to act on behalf of a user, not just generate output.

If you’re still unsure which one will help you meet your business goals, answering the following questions can help you choose:

1. Do you need AI to complete tasks on its own, without step-by-step human instructions?
2. Does the task involve multiple steps or interactions with tools/systems/APIs?
3. Should the AI be able to make decisions, adapt to feedback, or retry until the goal is met?
4. Are you okay with a lightweight system that follows a fixed plan but doesn’t reason or adapt?
5. Is your main goal for AI to generate content like text, images, code, or audio?
6. Does the content require human-like creativity or tone, rather than just templates?

For many businesses, however, the most powerful approach is to combine both: use generative AI for content and communication and agentic AI to handle processes and decisions. The key is identifying where AI can create the most value in your workflows—and choosing the right type to match.

When used together, generative AI and agentic AI can cover what needs to be created and how it gets done. Here’s how businesses can make the most of both:

Use Generative AI for Content, Agentic AI for Action

Let generative AI handle tasks like writing responses, summarizing documents, or generating marketing copy. Then, use agentic AI to decide when and where that content is used, who to send it to, and what to do next.

Example:
A sales assistant generates a follow-up email (generative AI), then autonomously sends it, logs it in your CRM, and schedules a reminder if there’s no reply (agentic AI).

Automate Decision-Making Loops

Generative AI can provide options, explanations, or creative inputs. Agentic AI can evaluate those options, test them, and adapt based on feedback, building dynamic workflows that learn and improve over time.

Example:
A product recommendation system generates ideas based on customer behavior (generative AI), and the agent tests different versions, monitors engagement, and updates future recommendations (agentic AI).

Use Generative AI Inside Agent Workflows

Treat generative AI as a tool that your agentic AI can call on when needed—just like it would call an API or access a database.

Example:
An agent managing customer support pulls in generative AI to draft a personalized response, summarize a past conversation, or translate a message—then sends it off automatically.

Combining both types allows businesses to scale creativity and decision-making. So, it’s not about choosing one over the other, but combining them for better results.

What are the key differences between generative AI and agentic AI? Both technologies can bring benefits to companies across industries, but they differ in purpose, function, interaction style, level of autonomy, and decision-making capabilities.

We recommend using generative AI when your focus is content creation and enhancing creativity or productivity, but going for agentic AI when you need goal-driven automation that can make decisions and complete workflows. And once you decide to build a GenAI tool or an AI agent for your organization, DevCom will gladly help. Contact us anytime.