Key takeaways
- Generative AI creates content, while agentic AI takes action to achieve goals.
- Use generative AI for ideas and drafts, and agentic AI for planning, coordination, and follow-through.
- Agentic AI adds autonomy by making decisions and completing multi-step work with minimal guidance.
- Together, generative and agentic AI support end-to-end workflows from creation to execution.
- You don’t need technical expertise to start using either—many tools already include both capabilities.
Think about the first time you saw an AI tool in action, instantly generating a paragraph, an image, or even a block of code from just a few words. It felt a little like peeking into the future. That spark comes from generative AI, a type of AI designed to create things based on your prompts.
But AI is quickly moving beyond creating and into doing. That’s where agentic AI comes in. Instead of just generating content, agentic AI can make plans, take actions, and work toward goals with only a bit of guidance from you.
Both are impressive, but they’re built for different kinds of tasks. Knowing how they differ can help you choose the right approach for your work and everyday tasks.
In this guide, we’ll explore what generative AI and agentic AI is, how they compare, and how you can use them to get more done with less effort.
Table of contents
- Agentic AI vs. generative AI: What’s the difference?
- How generative AI and agentic AI work
- Real-world examples of generative AI and agentic AI
- When should you use generative AI vs. agentic AI?
- How generative AI and agentic AI work together
- Benefits of generative and agentic AI
- Limitations of generative and agentic AI
- How can you start using generative AI and agentic AI tools today?
- What’s next for agentic AI and generative AI?
- Moving from output to results
- Agentic AI vs. generative AI FAQs
Agentic AI vs. generative AI: What’s the difference?
Generative AI creates content based on your prompts. Agentic AI, on the other hand, takes things further: It starts with a goal and determines how to achieve it, planning and executing the necessary steps with a high degree of autonomy.
What does generative AI do?
Generative AI produces new content—like text, images, video, or code—in response to what you ask it to create. You give it a prompt and it delivers something new, whether that’s an answer, an explanation, or content you can use directly. Think of it like having access to an always-on assistant that responds to your requests on demand.
You’ll find generative AI across many tools and categories. ChatGPT and Google Gemini are general-purpose tools that generate text and images. DALL·E and Midjourney focus on visual content, and GitHub Copilot helps developers write and understand code. While their outputs differ, they work in a similar way—you provide input, and the tool generates content in response.
Grammarly has generative AI features that you can prompt to create content that helps you brainstorm, draft in your own voice, refine ideas, improve clarity, revise, and more. Grammarly’s AI is built to enhance your writing wherever you work, making your communication more effective across your entire workflow.
What does agentic AI do?
Unlike generative AI, which responds to individual prompts, agentic AI can work toward a goal autonomously. It plans steps, makes decisions, uses tools, and adapts as it progresses—without needing constant instructions. You can think of agentic AI as more than a responder. It has agency: the ability to decide what to do next in order to complete a multistep task.
For example, in a work setting, you might give an agentic AI a goal such as “Prepare everything needed for next week’s client kickoff.” It could review background documents, draft an agenda, coordinate schedules, prepare briefing materials, send follow-ups, and flag open questions, checking in only when decisions or approvals are needed. Grammarly’s AI agents work proactively, leveraging your context to create more engaging, compelling content, communicate more effectively, and organize and manage your workday.
How generative AI and agentic AI work
While both types of AI are powerful, they operate in fundamentally different ways. Generative AI creates new outputs by predicting what should come next in a sequence, whereas agentic AI follows a loop of actions based on its environment, goals, and feedback. Let’s take a closer look at how each AI technology works.
How generative AI works
When you ask tools like Grammarly to write an email, the generative AI technology it employs isn’t pulling from a stored library of responses. Instead, it generates new content by predicting the most likely next token (a word or piece of a word) based on your prompt. The same principle applies to image generation—predicting the next pixel or feature.
These predictions are powered by a large language model (LLMs) that uses patterns it learns from billions of examples to predict what comes next. Each word it generates influences the next, creating a chain that allows the AI to create something original from your prompt.
How agentic AI works
Agentic AI works through a simple but powerful loop: perceive, plan, act, and learn. When you give an agentic AI system a goal, it gathers information and context, uses that information to make a plan, executes the plan across your tools and apps, and then evaluates the results to get better next time. It repeats this cycle until the goal is complete, adjusting its approach with what it learns along the way.
For example, imagine you’re managing a group project and want an agentic AI system to help streamline the work. The AI could pull key details from meeting notes or emails, then draft a plan that identifies priorities, owners, and deadlines. From there, it might create tasks, assign them to the right people, and check in on progress. You stay focused on the creative and strategic work while the agentic AI handles the coordination and administrative tasks.
Real-world examples of generative AI and agentic AI
Both types of AI are already part of everyday workflows, but they show up in different ways. Generative AI helps you create things; agentic AI helps you get things done. Here’s what that looks like in practice.
Generative AI examples
Generative AI is great when you need something written, summarized, or created on the spot. A few familiar use cases include:
- Turning a rough idea into polished writing: Give it a couple of bullet points, and it drafts the full email, message, or report section.
- Transforming material into new formats: Turn your notes into summaries, flashcards, quizzes, or scripts.
- Generating creative variations: Ask for multiple headline options, different tones, or alternative versions of a message.
- Designing visuals or concepts: Create images, mockups, or diagrams from a description.
- Enhancing clarity or tone: Improve a paragraph to sound more confident, concise, or audience-appropriate.
In short: You give direction, and it creates the content.
Agentic AI examples
Agentic AI shows up when you need more than content—it helps you move a task or project forward by taking action on your behalf. Here are some examples:
- Managing a multi-step research task: You share your topic, and it gathers sources (when connected), filters high-quality ones, builds an outline, and updates it as it finds more information.
- Keeping a document workflow on track: It monitors edits from reviewers, consolidates feedback, assigns follow-up tasks, and pings people who are behind.
- Handling follow-ups automatically: It watches for replies to important emails and sends appropriate follow-ups—or flags the ones you should handle personally.
- Planning and coordinating a project: You set the goal, and the system drafts the project plan, identifies owners, tracks progress, and tweaks the plan as things change.
- Running improvement loops: It checks results, identifies what worked or didn’t, and adjusts its next actions accordingly.
In short, you set the goal, and it plans, executes, and adapts along the way.
When should you use generative AI vs. agentic AI?
If you need help generating ideas, exploring topics, or creating content, use generative AI. If you want to automate a workflow or complete an action, use agentic AI. Use both when you need to create content and take action on it. Here’s a breakdown of how to decide:
Use generative AI when you want to:
- Draft, edit, or polish a piece of writing
- Design, make, or polish an image or other types of content
- Brainstorm ideas or explore new directions
- Summarize or reformat information into a different structure
Use agentic AI when you want to:
- Offload busywork like follow-ups, scheduling, or monitoring
- Automate tasks that have multiple steps or dependencies
- Move a project forward by organizing research, planning next steps, or coordinating contributors
- Keep track of progress and adapt as things change
Use generative AI and agentic AI together when you need to move from creating something to actually getting it done.
How generative AI and agentic AI work together
By using agentic and generative AI together, you can create end-to-end workflows. These technologies complement each other perfectly: Generative AI handles the creative heavy lifting, while agentic AI handles the follow-through and execution.
This combination is particularly powerful because it mirrors how many real-world tasks actually work. As a result, you can delegate time-consuming tasks to AI so you can focus on higher-level strategy and decision-making. Here are some examples of this power duo in action:
- Preparing presentations: Generative AI can help you brainstorm key points, research your topic, and draft slides with supporting visuals. Agentic AI can then organize the slides into a clear storyline, identify where additional information might be helpful, schedule practice sessions (when connected to your calendar), and send reminders as deadlines approach.
- Studying for tests: Generative AI creates practice questions, explanations, and study guides from your class materials. Agentic AI can turn those materials into a structured study plan, set reminders, track your progress as you complete practice activities, and adjust the plan based on areas where you need more focus.
- Creating social media content: Generative AI helps you brainstorm ideas, draft posts, and create graphics. Agentic AI can adapt content to your brand guidelines, schedule posts across platforms, and provide weekly summaries of engagement metrics so you know what’s performing well.
Benefits of generative and agentic AI
Generative and agentic AI are designed to help you work more efficiently—by supporting both creation and execution. Used together or separately, they can reduce busywork, accelerate progress, and help you focus on higher-value work.
- Faster creation and ideation: Both types of AI help you move quickly from a blank slate to something tangible—whether that’s drafting content, exploring ideas, or outlining next steps.
- Improved follow-through and execution: Generative and agentic AI can assist with turning ideas into action by handling repetitive tasks, coordinating steps, and supporting multi-stage workflows.
- Better focus on meaningful work: By offloading routine or time-consuming tasks, both forms of AI free up your time for creative, strategic, or decision-heavy work.
- Smoother end-to-end workflows: Together, generative and agentic AI can help you move from idea to outcome with less friction—supporting both the thinking and the doing along the way.
Limitations of generative and agentic AI
Despite their capabilities, generative and agentic AI have limitations that require active human oversight. Understanding these constraints helps you use AI more responsibly and effectively.
- Inaccurate or misleading output: Both generative and agentic AI can produce incorrect information or flawed conclusions, especially when prompts are unclear or data is incomplete. Always verify important outputs.
- Overtrust and reduced scrutiny: AI systems can appear confident even when they’re wrong. Avoid taking results at face value—review outputs, ask for sources, and apply your own judgment.
- Coordination and reliability challenges: When AI systems handle multiple tasks or interact with other tools, errors or conflicts can occur. Clear boundaries, testing, and monitoring are essential.
- Bias in outputs: Because AI learns from historical data, it can reflect existing biases. Regular review and correction are necessary to avoid reinforcing unfair or inaccurate patterns.
- Data privacy and security risks: Both generative and agentic AI often rely on user-provided information. Be cautious with sensitive data and review privacy and data-handling practices carefully.
How can you start using generative AI and agentic AI tools today?
Many tools now support both generative and agentic AI capabilities—and you may already be using a few without realizing it. The best way to get started is to begin small: Try a generative tool to spark ideas or draft content, then layer in agentic capabilities to help organize, coordinate, or move the work forward. As you experiment, you’ll start to build intuition for which tasks each type of AI is best suited for.
Here’s a simple approach to guide your first steps:
- Pick one task you repeat often: Choose something you do weekly, like sending out team updates, summarizing and organizing your class notes, or preparing for a client meeting. Starting with familiar tasks helps you spot where AI can make the biggest difference.
- Use a generative AI tool to draft, outline, or generate ideas: Try tools like Grammarly for writing support or DALL·E for quick graphics. Use generative AI to create a first draft, brainstorm directions, or polish what you already have.
- Add agentic AI to organize, execute, or automate next steps: Once the content is in place, use agentic AI to help with the follow-through—such as organizing your notes, creating a task list, sending or scheduling messages, or coordinating updates (depending on the integrations your tools support). Note: This doesn’t always require separate tools. Many generative AI tools now include agentic capabilities, and most agentic systems use generative AI behind the scenes.
- Review the results, refine your setup, and expand from there. Pay attention to what worked well and what felt clunky. Adjust your approach, then gradually apply this generative-to-agentic workflow to other areas of your work or studies.
What’s next for agentic AI and generative AI?
While generative and agentic AI have been evolving for years, they’re now becoming more capable, reliable, and embedded in everyday tools. The next wave of progress is less about entirely new breakthroughs and more about refinement: better memory, smoother coordination, and deeper context. Here are a few trends shaping what comes next:
- Smarter collaboration between agentic AI systems: Instead of one system trying to do everything, we’re moving toward coordinated “multi-agent” setups—where different AI agents specialize, then hand off work to one another. For example, one system might process meeting notes, another extracts action items, and a third drafts follow-up messages. It’s similar to having a small team working behind the scenes.
- Increased memory capacity: More AI systems will be able to remember your preferences, projects, and style across sessions. This helps reduce repetitive setup work—for instance, recalling your brand guidelines when you request a revised draft, or remembering how you prefer information structured.
- Greater contextual awareness: AI tools are getting better at understanding the broader context of your task, not just your prompt. With access to documents or schedules (when you grant permission), an agentic AI could infer the topic you’re studying or the deadlines you’re working toward and tailor its support accordingly.
- Broader adoption: Generative and agentic features are increasingly baked into tools you already use—email, calendars, writing apps, browsers—making them accessible without learning an entirely new set of skills or complicated software.
Moving from output to results
Agentic AI and generative AI are like having a creative partner and a project manager rolled into one. Every time you use Grammarly, generative AI and agentic AI converge into a single experience that helps you feel more in control of your workflow. Generative AI sparks ideas and creates content, while agentic AI turns those ideas into results by acting on your behalf. Built to support end-to-end writing and communication workflows, Grammarly’s AI agents go a step further—they analyze your goals, audience, and context to surface the most relevant suggestions as you work so you can take the next best action at the right moment.
Agentic AI vs. generative AI FAQs
Are AI and generative AI the same thing?
No. Generative AI is one type of AI that specializes in creating content, such as text, images, or code. AI is a broader field that includes many types of systems that can recognize patterns, make predictions, or take actions. Examples include voice assistants, recommendation systems, and fraud detection.
What is agentic AI vs. generative AI?
Generative AI creates content in response to prompts. Agentic AI takes a goal and plans actions to achieve it, handling steps and decisions along the way. You can think of generative AI as a creative partner and agentic AI as a coordinator that keeps the work moving.
Is ChatGPT generative AI or agentic AI?
ChatGPT is primarily generative AI because it produces content based on your prompts. However, newer versions include agentic features—such as browsing the web, calling APIs, or running code—that allow it to take certain actions when enabled.
When should I use generative AI vs. agentic AI?
Use generative AI when you want to create content quickly, explore ideas, or rewrite and refine what you already have. Agentic AI can help when you need to complete tasks, automate multi-step workflows, or offload follow-through work. Use both when you want AI to create something and act on it—for example, drafting an email, sending it, and scheduling a follow-up if you don’t get a response.
Can agentic AI and generative AI work together?
Yes, you can use both to create AI that can handle workflows from start to finish. Generative AI can create and polish content, while agentic AI handles execution, organization, and follow-through to turn those creations into results.
Does Grammarly use generative AI, agentic AI, or both?
Grammarly uses both generative AI and agentic AI. It uses generative AI to help you create or refine content—for example, by prompting it to help you rewrite text, adjust tone, improve clarity, and more.
Grammarly’s agentic AI capabilities can proactively assist you in the surfaces you’re working in, giving you suggestions and ideas based on your context and what you’re writing. Grammarly’s AI agents can analyze your writing context, surface relevant suggestions, and apply the ones you approve.
Together, Grammarly’s generative AI and agentic AI features help you move smoothly across every aspect of your workflow, get unstuck, and focus more on high-impact thinking.
