What Is Agentic AI? Definition, Examples, and How to Start Using It

Key takeaways

• Agentic AI can plan and take action on your behalf. This capability allows it to achieve multistep goals with minimal supervision.
• The real value is delegation. Agentic AI handles repetitive tasks so you can focus on creative, high-impact work.
• Humans still need to be involved. Understand how agentic AI systems make decisions, set clear boundaries, and always review their output.
• You can start today. Experiment with agentic AI using no-code tools that automate research, writing, and everyday tasks.

We’ve all been there: You’re finally settling in to tackle a big project—maybe a research paper or client presentation—something that demands real focus. But before you can get to the good part, you’re buried in the busywork: tracking down sources, fact-checking claims, and organizing your research. Hours slip by, along with your momentum.

Now, imagine if those repetitive tasks were just … handled so you could stay in flow and focus on the creative thinking that actually moves your work forward. That’s not wishful thinking—it’s the promise of agentic AI, a new kind of technology that can plan, decide, and take action on your behalf. Unlike AI assistants or chatbots that need constant prompting to work, agentic AI systems can act with a high degree of autonomy to achieve a goal. But how do they actually work, and what’s the best way to start using them?

This guide will walk you through everything you need to know about agentic AI: what it is (and how it relates to generative AI), how it works, what it can do for your workflow, and most importantly how you can start using it today to reclaim your time and focus on the work that matters most.

Table of contents

What is agentic AI?

Agentic AI refers to software systems that can take action to achieve a specific goal. Think of it as having access to an AI that doesn’t just answer your questions—it can act on your behalf to complete tasks and achieve goals. This ability to work independently transforms agentic AI from a simple tool into a capable partner that can take manual tasks off your plate.

What does “agency” mean in agentic AI?

As its name suggests, agentic AI has “agency”: the ability to make decisions and operate with a high degree of independence. Instead of waiting for your prompt, agentic AI can set a goal, create a plan to achieve it, and take the necessary steps to reach it, often without constant human direction. It’s the difference between a tool that waits for your input (like a spell-checker) and one that can independently plan, prioritize, and execute without you needing to constantly prompt it (like a writing assistant that rewrites for tone, clarity, and structure).

Grammarly’s AI agents are one example of how agentic AI can directly enhance your writing and communication workflows. These specialized writing agents don’t just work when you prompt them; they can proactively offer dynamic real-time suggestions based on the context of what you’re working on, helping you at every stage of the writing process.

Integrated seamlessly within your writing flow, Grammarly’s AI agents deliver relevant, context-aware feedback based on the type of writing you’re doing and the audience you’re writing for. They help refine complex elements like tone, conciseness, specificity, and logical progression, offering support at the right moment so you can communicate more clearly without losing focus on high-value work.

How agentic AI differs from other AI tools

Most AI tools today are reactive: They wait for your command and respond to individual prompts. That’s helpful for simple, one-off tasks. However, they fall short for complex goals that require multiple steps or ongoing decisions.

Agentic AI changes that dynamic. Instead of needing constant direction, it can take a goal, plan the steps, act on them, and adjust based on your feedback. Think of it as moving from “telling AI what to do” to “collaborating with AI that knows how to move the work forward.”

For example, if you ask an AI chatbot to find research sources for your upcoming report, it gives you a list and stops there. You still have to verify each source, find additional materials, and organize everything into a usable format. On the other hand, you can give agentic AI your research goal, and it works with you to find, verify, and organize your sources, moving the research forward while keeping you involved in key decisions.

Agentic AI vs. generative AI: What’s the difference?

It might seem counterintuitive, but generative AI and agentic AI aren’t opposites—they’re closely related and build on each other. Generative AI creates text, images, or ideas in response to prompts. Agentic AI builds on that capability to take action with what’s been created.

For instance, you can use generative AI to draft an email. With agentic AI, you can go further: It can send the email, track responses, and even follow up automatically to ensure you get a response.

In short, generative AI produces output; agentic AI delivers outcomes.

What can you do with agentic AI?

By taking care of the busywork and repetitive tasks that eat up your time, agentic AI can help you reclaim more time for work that matters. Here are specific examples of how it can help:

• Organize your research: Instead of waiting for prompts, your AI assistant can continually gather new research, evaluate sources for credibility, and organize them by topic. It can even flag outdated citations or gaps in your evidence—keeping your work accurate and up to date without the manual effort.
• Overcome writer’s block: When the system notices you’ve stalled, your agentic AI can step in with fresh momentum: drafting a detailed outline, restructuring your sections for clarity, or suggesting ways to strengthen your argument. It can help you regain flow before frustration sets in.
• Coordinate projects and tasks: Spending more time coordinating projects than doing the work? Let agentic AI take ownership of keeping everything on track. Give it your project goal and deadline, and it can monitor progress, proactively reach out when deliverables are at risk, escalate issues only when needed, and learn from each project to improve its coordination approach.
• Manage communication: Tell agentic AI your career networking or outreach objectives, and it can work with you to run the entire process. It can identify relevant contacts, craft personalized messages, send polite follow-ups when there’s no response, and schedule meetings when interest is shown.

How does agentic AI actually work?

When you give an agentic AI system a goal, like “create a study guide for my calculus test,” it doesn’t generate generic flashcards. Instead, it works with you to create a personalized study guide by reading your syllabus, analyzing your quiz performance, and sourcing target practice problems. This all happens through an ongoing four-step process that repeats until the AI has achieved its goal:

• Perceive: The system gathers and evaluates the information it needs to understand your goal. For example, it might review your syllabus, identify the main topics, and read your notes to figure out what matters most.
• Plan: The system decides which tasks to execute to achieve the goal, using the context from the previous step. It could decide to prioritize topic areas based on your past quiz performance, determine that you need more concept review in some areas and practice in others, and emphasize visual over textual explanations (given your learning style).
• Act: The system puts its plan into action: sourcing relevant practice problems from multiple databases, generating customized explanations that match your learning style, and organizing everything into a logical study sequence.
• Learn: The system continuously adapts based on your performance and feedback. For instance, if you have struggled with specific problem types, it will remember to adjust future study guides to include more foundational work in those areas.

But how do agentic AI systems complete this loop?

The core components behind agentic AI

Behind the scenes, four key pieces work together to help agentic AI systems perceive, plan, act, and learn:

• Perception: Just as you’d start a project by gathering context, agentic AI begins by analyzing your existing sources, such as emails, notes, or spreadsheets, to get up to speed. It looks for patterns and relationships, like recurring questions in past assignments or topics you might have struggled with before. It can also monitor these sources for updates, automatically springing into action when new content appears (such as updates to your syllabus or new practice questions).
• Decision-making: To plan its next step, the AI uses its “brain,” made up of large language models (LLMs) and reasoning techniques, to identify potential actions and prioritize them. For example, in reviewing your quiz scores, it might notice that you struggle more with word problems than formulas and then decide to focus the entire study plan around practicing problem-solving rather than memorizing concepts.
• Execution: To carry out its plan, agents connect directly to your tools—like your inbox, calendar, and document editors—through application programming interfaces (APIs) and integrations. This allows it to take real action on your behalf, like drafting study guides and sharing them with your study group, organizing your notes into folders, and blocking time on your calendar for study sessions.
• Memory: The agent remembers what it’s learned—which explanation styles work best for you, where you typically struggle, and your preferred study format—so it can match your style and get better at achieving its goal over time.

Put simply, these four capabilities enable agentic AI to operate with a high degree of autonomy: It perceives its environment, reasons through options, acts on your behalf, and refines its approach as it learns what works.

What are the main types of agentic AI?

There are many different types of agentic AI systems, each designed for specific goals and challenges. Here’s an overview of the two main types of systems:

• Single agent: The solo worker. One AI system handles your entire task from start to finish. It’s ideal for straightforward, repeatable tasks that don’t need specialized expertise, like summarizing your meeting notes.
• Multi-agent: The collaborative team. Multiple AI assistants team up, each taking on different pieces of the work. It works well for multistep tasks, like creating study guides: One system handles research, another writes summaries, and a third creates practice questions. When multiple agents are coordinated and managed across tools and tasks like this, it’s called orchestration.

While single and multi-agent systems can get you very far, some tasks require a high degree of organization or special skills to be effective. That’s where horizontal and vertical multi-agents come in:

• Horizontal multi-agent: The specialist team. Like multi-agents, these are teams of systems with a twist. Each system brings a unique specialty to the table. Think of it like a sports team, where each player has a position—defending, passing, and scoring—but they all work together to win the game. This system is great for projects that require a lot of specialized expertise, like creating pitch decks where one system designs slides, another crafts the story, and another builds persuasive arguments.
• Vertical multi-agent: The structured team. Similar to a company hierarchy, there are two levels of agents. Junior-level agents handle simpler tasks (like gathering information), while senior-level agents tackle complex work (like quality control and final editing). This system works well for large projects with multiple stages, like a research paper project where junior systems write sections and senior systems outline and edit the content.

Here’s a side-by-side comparison of all these systems:

What can you do with agentic AI today?

Agentic AI isn’t about futuristic robots or sci-fi assistants. It’s already here, quietly taking care of the repetitive, time-consuming work that slows you down. These systems can set goals, make plans, take action, and adapt based on results—all so you can stay focused on what matters most.

Here are a few ways agentic AI can help today:

• Research smarter: Give your agent a topic or goal, and it can gather relevant insights, organize sources, highlight key takeaways, and even update your notes when new information appears—keeping your work current without constant effort.
• Learn faster: An AI study partner can plan sessions, quiz you on weak spots, and adjust lessons as you progress, acting like a personal tutor that knows how you learn best.
• Stay organized: A planning agent can prioritize tasks, sync with your calendar, and reschedule commitments automatically when plans shift so your schedule stays balanced and productive.
• Handle customer questions: A support agent can manage incoming requests, answer common questions, and flag complex issues for human review—learning from every interaction to respond more effectively over time.
• Write and edit effectively: A writing agent can draft, edit, and refine messages or documents in your tone of voice, learning your preferences for clarity, structure, and flow as you keep using it.
• Brainstorm creatively: A creative agent can help you develop ideas for campaigns, names, or projects, refining them through ongoing feedback and turning raw thoughts into actionable next steps.
• Analyze business data: A data agent can pull metrics from different sources, track trends, flag anomalies, and deliver summaries automatically, giving you a clear view of what’s happening without manual analysis.

What are the advantages of agentic AI?

Agentic AI isn’t a distant vision. It’s already helping people handle repetitive, time-consuming work so they can focus on what matters most. Here are a few ways it can help you today:

• Reduce busywork: Let agentic AI handle the tedious tasks that eat up your day. Working on a pitch deck? Have the AI research benchmarks and format your slides while you focus on crafting compelling arguments.
• Tackle complex projects: Daunting becomes doable when agentic systems break the work into focused steps. Writing a term paper? One agent researches the topic, another analyzes grading criteria, and a third organizes sources by themes.
• Receive feedback that fits your style: Instead of generic feedback, agentic AI learns your voice and goals to provide writing suggestions that actually help. For instance, it can review marketing copy and offer suggestions that align with brand guidelines and campaign goals.
• Delegate coordination tasks: Use agentic AI to manage back-and-forth communication during a project. It can assign tasks, ask for status updates, and flag when something’s falling behind—freeing you to focus on strategy.
• Get help before you need it: Instead of remembering to ask for help, agentic AI proactively offers support. Meeting with a prospect? It can research recent company news and find relevant case studies beforehand.

What are the limitations of agentic AI?

Agentic AI can feel magical, but like every technology, it has challenges and pitfalls. Most can be managed with curiosity, caution, and common sense. Here are some challenges worth knowing about:

• Hallucination: Agentic AI can sound really convincing, but that doesn’t mean it’s right. Sometimes, like when it lacks knowledge or has outdated data, it can make stuff up. Always double-check important details against trusted sources.
• Overtrust: “Don’t believe everything you read on the internet” applies to agentic AI. Instead of blindly trusting its output, treat it as a draft and review it carefully before publishing.
• Transparency: If you can’t tell how an agentic AI got its answer, you’re not alone—many systems don’t explain their steps. Ask it to outline its reasoning when possible and double-check the logic.
• Bias: Agentic AI systems learn from datasets built by people, so they can pick up unfair or biased patterns. Regularly review the system’s output and make changes if you see anything wrong.
• Coordination issues: Just as in human teams, multiple AI systems can get tangled without clear communication, leading to duplicated tasks or conflicting results. Define clear roles and test processes regularly to ensure the systems are working in sync.
• Data privacy: Agentic AI needs data to function, but sharing sensitive information creates privacy risks. Avoid sharing personal details or confidential information and review your privacy settings.
• Skill erosion: You can have too much of a good thing. If you’re relying on agentic AI to do the creative, strategic work, your own skills can fade. Use it as support, not a crutch, and keep meaningful work for yourself.

While agentic AI does have its challenges, you can overcome them. By constantly reviewing its output, setting clear guardrails, and being intentional in how you use it, you can get the most out of these systems while minimizing their downsides.

Why humans need to be in the loop for agentic AI

As you start using agentic AI, remember: You should stay involved to guide, review, and approve the system’s work. Otherwise, agentic AI systems might unintentionally misuse access, promote harmful patterns, or produce results that seem right but aren’t. You should ask them to lay out their assumptions, review their outputs, and catch mistakes before they cause harm. This way, you can create systems that are useful, reliable, and trustworthy.

How to get started with agentic AI

There are plenty of tools that can help you get started with agentic AI. Chances are, you’re already using some of them. The key is to experiment: Start small, review carefully, and then expand to other cases as you see fit. Here’s a simple process to help you get started:

1. Identify a repeatable workflow: Look for a process you do often that involves multiple steps or tools—something an agent could manage end to end. For example, following up after meetings, organizing research across documents, or managing project reminders.
2. Use an approachable tool: Many popular products today have agentic AI features. Choose a tool that you’re already familiar with and that fits naturally into your routine. No-code tools like Grammarly or Superhuman are good starting points, as they integrate seamlessly into your daily workflow.
3. Set clear goals, not just tasks: Instead of telling the system what to do (“summarize this email”), describe the outcome you want (“keep my inbox organized by summarizing and tagging important threads”). This helps the agent plan and execute steps more independently.
4. Observe and refine: Review how the agent approaches your goal. Does it take the right actions? Miss any context? Adjust its permissions, rules, or inputs as needed. Think of this as training the system to work the way you do.
5. Expand gradually: Once you’re confident it’s handling smaller tasks effectively, experiment with more complex, multistep goals, like preparing client updates, organizing reports, or coordinating project timelines across teams.

Getting started with agentic AI doesn’t mean you need to make big changes in how you work. Start small, test, and iterate before scaling up. The more you explore, the easier it will feel to keep using it.

Putting agentic AI to work

Agentic AI may sound futuristic, but its impact is already tangible. These systems can reclaim hours of focus, reduce friction, and help you communicate ideas with greater clarity, all by managing the repetitive work that gets in your way.

You don’t need technical expertise to begin. Start small with the tools you already use, let the AI handle simple workflows, and gradually build from there. As you experiment, you’ll discover how seamlessly agentic AI can work alongside you, amplifying your focus, creativity, and impact.

Grammarly’s AI agents offer a practical entry point into agentic AI because writing and communication make up such a significant part of daily workflows. Built directly into the tools you already use, these agents work proactively as you work, helping with tasks like drafting, summarizing, revising, and more. These AI agents deliver context-aware suggestions that adapt to your audience, goals, and content type, helping you do your best work and feel more in control of your day-to-day.

Agentic AI FAQs

What’s the meaning of agentic AI?

Agentic AI refers to AI systems that achieve a goal with a high degree of autonomy. Instead of waiting for instructions, they can plan steps, make decisions, and act independently within defined boundaries.

What is the difference between AI and agentic AI?

AI is a broad term for machines that can process information and generate outputs, while agentic AI goes a step further by acting autonomously toward a goal. In other words, traditional AI answers questions, and agentic AI takes action.

Learn more about AI in our guide: What is AI?

Yes, you can use agentic AI without coding by using everyday tools like Grammarly and Superhuman, which already include agentic features to automate repetitive tasks.

For example, Grammarly’s AI agents work proactively and seamlessly across your workflow, offering context-aware suggestions that adapt to your audience and goals—no coding or technical setup required.

Is ChatGPT an agentic AI?

ChatGPT itself isn’t fully agentic because it waits for your prompts to do something. But when connected to external tools that enable it to take actions (such as searching, summarizing, or scheduling), it can operate in an agentic way.

Learn more about ChatGPT in our guide: What is ChatGPT?

Does Grammarly have agentic AI?

Yes, Grammarly’s AI agents are seamlessly integrated into the product experience. Think of them as an always-on, context-aware team that steps in exactly when you need them for the task at hand. They’re designed to proactively help you communicate more effectively—for example, by offering personalized feedback tailored to your target reader, providing additional evidence, fact-checking your argument, and suggesting ways to make your writing sound more confident and clear.

Learn more about Grammarly’s AI agents here.