Agentic AI vs. AI Agents: What’s the Difference and Why it Matters

Key takeaways

• Agentic AI is the “how,” and AI agents are the “who.” Agentic AI is the method that gives systems the ability to plan and make decisions. AI agents are the tools that use those abilities to actually do the work.
• They work best together. Agentic AI handles the reasoning, while AI agents carry out the tasks—so work feels faster, smoother, and more intuitive.
• You can use them to save time and focus. These tools can handle routine tasks, letting you spend more energy on creative or meaningful work.
• No tech expertise required. Most AI agents and agentic AI systems are designed for anyone who wants to automate tasks and work smarter, not harder.

In the world of AI, there are two powerful developments: AI agents and agentic AI. These technologies promise to handle tasks like booking meetings, creating study guides, and organizing research. These terms sound similar, so they must be the same, right?

Not quite. While both involve AI that can act on your behalf, there are subtle yet important differences in what they can do and how they function. Understanding this distinction can help you choose the most effective AI tool for your specific situation.

In this article, we’ll unpack agentic AI versus AI agents—what each means, how they work, and how you can actually use them—to get the most out of these powerful technologies.

Table of contents

• What is the difference between agentic AI and AI agents?
• How agentic AI and AI agents work together
• How AI agents work
• Types of AI agents
• Real-world examples of agentic AI and AI agents
• Benefits of using agentic AI and AI agents
• Limitations of using AI agents and agentic AI
• How can you start with agentic AI and AI agents
• What’s next for agentic AI and AI agents
• Practical next steps
• Agentic AI vs. AI Agents FAQ

What is the difference between agentic AI and AI agents?

Agentic AI is the “how” behind creating systems that can plan, act, and adapt on their own. It’s rooted in the idea of agency—the ability to make decisions and take action without constant human input. Think of it as a blueprint for building intelligent, independent systems.

AI agents, on the other hand, are the “who” that get things done. They’re the tools that carry out the blueprint, designed for specific purposes like summarizing notes, managing projects, or analyzing data.

Here’s where it gets interesting: The relationship isn’t one-to-one. All agentic AI systems function as AI agents, but not all AI agents are truly agentic. Some agents simply follow pre-set rules to complete simple, repeatable tasks. For more complex work, agentic AI is what gives an agent the ability to plan, problem-solve, and adapt as things change.

For example, think about working toward a goal with multiple moving parts, like creating a research plan or coordinating a product launch.

• An AI agent might carry out individual steps you assign, like drafting an outline or organizing data you provide.
• An agentic AI could take the overall goal, determine the best sequence of steps, identify gaps along the way, and adjust its approach as new information comes in.

In short: AI agents execute tasks; agentic AI drives outcomes.

Examining how Grammarly’s AI agents work exemplifies complex agentic AI in action, where agents can plan, problem-solve, and adapt in order to help at every stage of your workflow. Rather than waiting for prompts, these agents work proactively as you type, from brainstorming to searching for and retrieving knowledge to drafting and revising content to managing action items and next steps.

Whether you’re drafting an email, refining a report, or summarizing ideas, Grammarly’s AI agents analyze your context to adapt suggestions to your audience and goals and provide relevant suggestions when you need them. These agents help you communicate more clearly and confidently while keeping your focus on the work itself.

Why does this distinction matter?

Understanding the difference between agentic AI and AI agents isn’t just technical; it’s practical. Agentic AI defines how systems think and make decisions, while AI agents are the tools that bring those decisions to life.

This distinction explains why some AI tools feel smarter or more adaptable than others. Knowing which is which helps you choose the right tool for your needs—whether you want a simple assistant to automate tasks or a more advanced system that can plan and reason on its own.

How agentic AI and AI agents work together

When you put agentic AI and AI agents together, you get something pretty powerful: semi-autonomous systems that can handle complex workflows from planning to execution. Agentic AI provides the thinking and reasoning capabilities, which AI agents rely on to do the work and adapt when things change. The result? You reclaim time for creative and strategic work, rather than getting bogged down in details.

Say you’re planning a group project and want to use an AI agent to help organize it. That agent might use agentic AI capabilities under the hood to break down the project, prioritize tasks, and suggest owners. Then, it can handle the logistics: creating tasks, assigning people, and checking on progress. You stay focused on the creative or strategic parts, while the AI manages the busywork.

Now that we’ve seen how they work together, let’s zoom in on the “doers” themselves, AI agents, and understand how they operate.

How AI agents work

AI agents work through a simple but powerful loop—perceive, plan, act, and learn—to achieve goals with minimal supervision. They gather information and context, use it to decide which actions to take, execute those actions by connecting to your tools, and then adapt based on your feedback. Simple agents might follow predefined rules, while more sophisticated ones use agentic AI (often powered by large language models) for complex reasoning and planning. Whether they’re helping you write emails or organize your research, agents are continuously running through this four-step process to carry out tasks and get better at helping you.

For a deeper dive into exactly how this process works with real examples, check out our guide to AI agents, which walks through each step in detail.

Types of AI agents

There are several types of AI agents, each designed to solve different problems, ranging from simple rule-based agents to more advanced reasoning systems. They’re often grouped into two tiers: a core group of the most foundational agents and advanced agents that build on them to handle more complex challenges.

Here’s a brief overview of the five core types:

• Simple reflex agents follow pre-programmed “if X, then Y” rules, but can’t handle situations outside of those rules.
• Model-based reflex agents also rely on rules, but can track what’s happened and adjust if their environment changes.
• Goal-based agents can plan and carry out the steps needed to achieve a goal.
• Utility-based agents are similar to goal-based agents, except they can weigh different options for achieving a goal and choose the one that provides the most value.
• Learning agents learn from experience and use feedback to improve their performance.

Advanced agents build on these core types to take on more complex goals:

• Multi-agent systems (MAS): Teams of agents that work on bite-size tasks to achieve a shared goal.
• Hierarchical agents: A more organized version of MAS, where a “manager” agent assigns tasks to “worker” agents.
• Hybrid-composite agents: These agents blend different types together (like reflex, goal-based, and learning) to balance speed, adaptability, and intelligence.

Our guide to the different AI agent types provides more detailed examples of how each type works and how to choose the right kind of agent for your task.

Real-world examples of agentic AI and AI agents

Modern AI tools increasingly combine agentic intelligence—the ability to reason, plan, and adapt—with agents that carry out actions to achieve specific goals. Together, they power many of the experiences people already rely on today.

Let’s look at how this plays out in practice across common use cases.

• Writing and editing: AI writing tools can do more than just correct grammar or paraphrase sentences. The agentic component identifies your writing goal—such as improving tone, clarity, or persuasiveness—and plans how to achieve it. The agents then execute those steps by generating rewrites, inserting suggestions, and applying style edits in real time. The result is a writing assistant that not only reacts to input but also anticipates what will strengthen your message.
• Research and information gathering: When researching a topic, agentic AI can outline a plan: determining what information is needed, identifying credible sources, and deciding how to structure findings. AI agents then follow that plan—searching databases, summarizing key points, and organizing results. This collaboration turns hours of manual searching into a focused, goal-driven process.
• Learning and studying: In adaptive learning tools, agentic AI monitors progress and reasons about what to focus on next. It sets goals—like reinforcing concepts that need more practice or introducing new material at the right pace—while AI agents act on that plan by generating personalized quizzes, examples, or explanations. Over time, the system learns how to keep each learner challenged and supported.
• Creative brainstorming: For creative tasks, agentic AI interprets the intent behind a prompt—such as brainstorming ideas for a campaign or product name—and develops a plan to explore different directions. The agents then generate and refine options, learning from feedback as they go. This pairing turns creativity into a dialogue: The system proposes, tests, and evolves ideas collaboratively.
• Project management: In project management tools, agentic AI oversees overall goals and dependencies. When it detects delays or shifting priorities, it replans the workflow and determines next steps. The agents then act—updating timelines, reassigning tasks, and notifying teammates automatically. The outcome is a system that doesn’t just track progress but actively helps keep work on course.

Benefits of using agentic AI and AI agents

Agentic AI and AI agents work together to take on repetitive tasks and assist with more complex work, freeing you to focus on creative and strategic thinking. In general, agentic capabilities give AI systems the ability to plan, reason, and adapt, while agents carry out those plans through specific actions. Here’s how they can help in practice:

• Reduce busywork: These systems excel at automating repetitive, time-consuming tasks that don’t require deep expertise. Working on a pitch deck? Agentic AI can outline a plan to gather market insights or verify statistics, while AI agents execute that plan and refine their approach based on your input—so you can focus on building a compelling story.
• Get more relevant feedback: Instead of offering one-size-fits-all suggestions, these tools can learn from your style, goals, and context to provide feedback that fits your work. For example, an AI assistant could review marketing copy with your brand tone in mind or adjust its feedback on academic writing to align with a professor’s expectations.
• Make complex projects manageable: Large projects can feel overwhelming. Agentic AI can help by breaking them into clear, achievable steps, while AI agents handle execution—researching, summarizing, and organizing information as they go. Together, they turn complex tasks like writing a research paper into a structured, step-by-step process.
• Streamline coordination: Managing progress and communication can take as much time as the work itself. AI systems with agentic capabilities can monitor timelines and dependencies, while agents take action—assigning tasks, updating statuses, or flagging risks before deadlines slip. That way, you can stay focused on outcomes instead of logistics.
• Get help before you need it: Rather than waiting for you to ask, agentic AI can reason about your upcoming goals and surface helpful next steps. If you have an exam coming up, for instance, it might identify areas that need review, while AI agents generate practice questions or study materials that adapt to your performance.

Limitations of using AI agents and agentic AI

While AI agents and agentic AI are powerful technologies, they have real shortcomings—namely, they can make mistakes, unintentionally misuse sensitive data, and can become a crutch if you rely on them too heavily. These challenges aren’t reasons to avoid the technology, but they do require careful design, review, and governance. Below are the most important concerns and practical mitigations to keep in mind.

• Hallucinations: When these systems hit a snag—like a knowledge gap or outdated information—they can hallucinate (i.e., make things up). Unfortunately, these erroneous statements often sound really plausible. Always verify important facts with trusted sources and ask for citations when possible.
• Overtrust: Trust should be earned, and that’s true for AI as well. Instead of accepting everything at face value, treat AI-generated content as a first draft. Review it carefully, fact-check key claims, and add your own judgment before publishing it.
• Transparency gaps: These systems can often feel like a black box. You throw them some instructions and get the output, but you have no idea what happened in the middle. You’re left guessing at their reasoning. Instead, ask the system for its sources or the reasoning behind its output so you can verify its assumptions.
• Coordination challenges: Sometimes AI agents step on each other’s toes. When groups of AI agents work together, without clear coordination, they can duplicate efforts or produce conflicting results. Avoid confusion by defining specific roles for each system and testing your workflows regularly to catch any problems.
• Bias: AI learns everything it knows from us, including our meaner tendencies. AI systems learn from human-created data, which means they can absorb societal biases and unfair patterns. Regularly review outputs for potential bias and correct problematic outputs.
• Data privacy and security: What you share doesn’t always stay between you and your AI. These systems need information to help you, but there’s a possibility that your inputs could leak. Try to avoid sharing confidential or sensitive information, and review your privacy settings to ensure that the system is properly using and storing your data.
• Skills maintenance: Just like how your bicycle can get rusty if it isn’t used, the same can happen to your own abilities. Using these tools as a crutch can diminish your creative and strategic skills over time. Be thoughtful about how you use AI—delegate manual work to it while keeping the meaningful, challenging work for yourself.

Here’s a side-by-side look at all the significant limitations of these technologies and how to overcome them:

How to stay in control of your AI tools

Using these tools can feel like unlocking a cheat code, but here’s the thing: You should always stay in the driver’s seat. Be intentional with how you use the tool, guide the process, review the outputs, and make the final calls. If you skip these steps, you might find yourself in hot water, like accidentally publishing inaccurate results or letting your skills get rusty over time.

Another upside of staying involved is that you’ll quickly see where these tools excel and where they fall short. These insights will help you develop an intuition about where to use them to get the most benefit.

How to start with agentic AI and AI agents

Getting started with agentic AI and AI agents doesn’t have to be a big, laborious project. Instead, experimenting works best. Start small, review the output, and then try them on other tasks. Here’s a walk-through of a process you can use:

1. Identify a repetitive task: You know those time-suck tasks that take you away from meaningful work—like summarizing articles, sending follow-up emails, organizing notes, or formatting slides? Those are ideal first tasks for these tools.
2. Choose the right approach: Many popular products today have AI agents and agentic AI capabilities. For simple, rules-based tasks, start with AI agents in tools you already know—use them to summarize notes, format documents, or send standard follow-up emails. For complex tasks that require planning and reasoning, like analyzing research trends or managing multistep workflows, look for agentic AI features that support nuanced decision-making.
3. Review and refine: Look at the results to ensure they align with your expectations. If they don’t look right, give the AI agent feedback. The more guidance you provide, the better the agent can match your preferences.
4. Expand gradually: Once you’re happy with the results and have a better understanding of the agent’s strength, you can build more complex agents to assist with larger tasks, like building a pitch deck.

What’s next for agentic AI and AI agents

Right now, most AI tools work in isolation—you have to juggle multiple systems or rely on one AI that tries to do everything but isn’t great at any of it. As more tools support AI agents and agentic AI becomes more powerful, this will change, resulting in greater autonomy and collaboration. Here are some trends worth keeping an eye on:

• Teams of agents working together: We’re already seeing groups of agents working together, handling complex projects from start to finish with minimal human oversight. This trend is only going to grow, since these agentic systems are more efficient and can take on larger projects. Soon, you might have a personal AI assistant that automatically coordinates with your colleagues’ assistants to schedule meetings, prep agendas, and follow up.
• Specialized agents: Instead of one AI trying to do everything, we’ll see agents designed for specific roles, like writing experts, data analysts, or research specialists. These agents can plug into your existing tools and work alongside human teams. Soon, you’ll have your own squad of AI writing specialists, bringing specialized knowledge (like scientific writing or marketing copywriting) to give suggestions that help you take your work to the next level.
• Agents working across tools: Today, agents are often built directly into the software you already use: your email app, calendar, documents, and project management tools. Soon, these agents will communicate with one another across different tools, reducing the amount of copy-pasting and manual coordination you have to do. Imagine your email AI automatically updating your project management tool when a client responds, while your calendar AI reschedules related tasks based on the new timeline.

Practical next steps

Agentic AI and AI agents are like Batman and Robin: The two work best when they’re together. Agentic AI is the approach to creating systems that can plan, think, and reason, while AI agents are programs that can get things done, either with or without agentic AI. Put them together, and you get powerful, mostly independent systems that can handle tasks from start to finish with minimal user input.

Grammarly’s AI agents show how this pairing works in practice. Built on agentic AI, the AI agents don’t just respond when you ask for help; they work alongside you in real time, proactively offering suggestions based on the document you’re writing, who you’re writing to, and what you’re trying to achieve. Because they’re integrated directly into your existing writing tools, Grammarly’s AI agents can deliver context-aware feedback at every stage of the process, from early brainstorming to final polish. They help you refine tone, clarity, conciseness, and structure as you write. As a result, you can move faster, stay focused on high-value work, and communicate with more confidence.

Agentic AI vs. AI Agents FAQ

How does agentic AI differ from AI agents?

Agentic AI is a design approach or capability that enables AI systems to reason, plan, and make decisions autonomously. AI agents are the actual programs that use these capabilities (or simpler, rule-based logic) to complete tasks. Think of agentic AI as the blueprint for building intelligent, goal-oriented systems, and AI agents as the tools built from that blueprint.

What is agentic AI?

Agentic AI refers to AI systems designed to achieve goals with a high degree of autonomy. Rather than waiting for specific instructions, these systems can plan steps, make decisions, and take action within defined limits, adjusting as they gather new information.

What does agentic mean in AI?

“Agentic” in AI means agency: the ability for systems to reason, plan, and make decisions to achieve goals with minimal human guidance. It’s the key ingredient in building autonomous systems that can tackle tasks from start to finish without constant human guidance.

How do AI agents work with agentic AI?

Agentic AI gives AI agents strategic thinking and planning capabilities, which agents can then use to handle complex, multistep tasks. This powerful combination allows you to build semi-autonomous systems that can achieve complex goals without constant human guidance.

Does Grammarly have agentic AI and AI agents?

Yes, Grammarly has agentic AI systems and AI agents that act as your trusted, personal team of helpers that help you draft, summarize, and revise your best content across the writing tools you already know and love. They can offer personalized feedback tailored to your target reader, provide additional evidence and fact-check your argument, and offer suggestions to make your writing sound more confident and clear.

Learn more about Grammarly’s AI agents here.