AI Assistants vs. AI Agents: What’s the Difference and When to Use Each

Key takeaways

• AI assistants wait for commands before responding, while AI agents can take a goal and make a plan to achieve it.
• AI agents and assistants use the same underlying technology, but agents have autonomy, coordination, and memory capabilities to handle multistep tasks.
• Use assistants for quick, simple tasks, agents for broader, goal-based projects, and combine the two to handle complex, multistep workflows.
• You don’t need technical skills to benefit—many tools you use support assistant and agent capabilities.
• The best outcomes with assistants and agents come from regular human oversight and feedback.

Today’s AI tools, like assistants and chatbots, can help with many everyday tasks, such as explaining complex concepts and turning messy notes into a clean outline. But what if AI could help beyond a single step? Imagine giving it a goal, such as creating a report, and having it support the process by planning next steps, drafting content, checking sources, and routing work for feedback.

That’s where AI agents come in. While AI assistants and agents often share the same underlying technology, they’re designed for different kinds of work. Assistants respond to individual requests, while agents help coordinate broader, goal-based workflows that span multiple steps and tools, keeping you involved through feedback and review.

In this guide, we’ll break down AI assistants versus AI agents: what each does best, where they overlap, and how to use them together to support more complex workflows.

Table of contents

• What’s the difference between AI assistants and AI agents?
• How do AI assistants and agents work together?
• When to use an AI assistant vs. an AI agent
• Advantages of using AI assistants and agents
• Limitations of using AI assistants and agents
• How to get started with AI assistants and agents
• Choosing the right AI for the job
• AI agents vs. AI assistants FAQs

What’s the difference between AI assistants and AI agents?

AI assistants respond to commands to complete individual tasks, while AI agents act more autonomously, helping you work toward a goal by planning and carrying out multiple steps while looping you in along the way.

Here’s the difference in action: An assistant can help you summarize your notes for a project kickoff meeting—but you have to ask. An agent, on the other hand, can help you make the meeting successful by organizing the notes, adding action items to your project management tool, and scheduling a follow-up meeting, checking in with you for feedback as it goes.

A good way to understand how AI agents go a step further is by seeing how Grammarly’s AI agents help at every stage of your workflow. Rather than simply responding to a prompt to revise text, these agents work proactively, surfacing context-aware suggestions across your entire content creation process. For example, they can help you brainstorm, search for and retrieve knowledge, predict your audience’s reaction, draft and revise content, and manage your actions and next steps. Built into the tools you already use, Grammarly’s AI agents help you create and share content that is clear, compelling, and authentically yours.

What are AI assistants?

AI assistants, like chatbots, scheduling bots, and writing helpers, are reactive tools designed to handle one-off or guided tasks. You usually need to tell them what you need, creating a “prompt-response” loop where the assistant never takes the first step. It’s like a tennis match where you always have to serve.

Most AI assistants are powered by large language models (LLMs) that understand natural language. You’ve likely used some of these already, whether it’s conversational chatbots like ChatGPT, Claude, and Gemini, or voice assistants like Siri and Alexa. All follow the same pattern of responding to what you ask rather than anticipating what you might need.

What are AI agents?

​​AI agents are semi-independent systems that can plan and complete tasks to achieve a goal. Unlike assistants that wait for commands, agents can work through complex workflows with less step-by-step direction—often after you give them a goal—and loop you in for feedback when needed.

Underneath the hood, agents can look similar to assistants: They’re also often built on LLMs and have capabilities like memory and integrations with your apps and tools. But what sets them apart is how they use these capabilities to achieve goals. For instance, agents use memory to remember feedback and results from previous interactions to deliver better results over time, while the integrations help them act on your behalf and carry out tasks.

This combination of planning, memory, and integration allows them to support multistep workflows with minimal guidance. For example, an agent might automatically update your study guide with new lecture notes or monitor your project management tool and send weekly progress reports—without you having to remember each step.

Our guide to AI agents covers more information about how AI agents work and other real-world examples of how they’re used today.

How do AI assistants and agents work together?

Many modern tools combine assistants and agents: Assistants handle prompts, while agents support multistep work behind the scenes. Think of it like a restaurant—you place your order with the waiter (the assistant), while the kitchen (the agent) prepares the meal.

Here’s how this partnership works in practice. When you ask an assistant to help research a topic for your next term paper, it becomes your primary point of contact. It might ask clarifying questions or give you progress updates along the way.

Meanwhile, the agent gets to work on your request. It breaks down your goal into actionable steps and coordinates multiple tasks without requiring constant input from you. The result? You simply tell the assistant what you need, and the agent makes it happen.

When to use an AI assistant vs. an AI agent

A rule of thumb: Use AI assistants for simple, guided tasks and AI agents for complex, goal-based workflows. Here’s a detailed comparison table to help you see the differences across common scenarios:

Advantages of using AI assistants and agents

Both AI agents and assistants bring their own unique value—assistants help you accomplish tasks faster in an approachable way, while agents can take on increasingly complex workflows without constant input. Understanding the benefits that AI agents and AI assistants offer helps you choose the right combination for your work.

Advantages of AI assistants

An AI assistant is like a smart colleague who’s always available to help with quick questions and tasks, allowing you to get help with your work without ever fully taking over. Here’s why they’re helpful:

• Simple setup: Most AI assistants work right out of the box, meaning no complicated configurations or training periods. You ask in plain language, and they help.
• Instant help: When you’re stuck on something, assistants give you immediate answers. Need a quick grammar check or a better way to phrase something? Done in seconds.
• High control: Assistants work one step at a time and act only when you ask them to, making it easier to review and adjust outputs before using them.

Advantages of AI agents

AI agents are more like a dedicated project manager who can help manage complex workflows over time. Instead of handling one task at a time, agents can break a goal into steps, coordinate work across tools, and move the process forward with less step-by-step input from you—looping you in for feedback when needed.

• Handle complexity: Agents can help coordinate multiple moving parts at once, such as tracking deadlines, organizing inputs from different stakeholders, and keeping related work connected across tools.
• Reduce cognitive load: While you still check in periodically, agents can keep work moving in the background, maintaining project state, tracking progress, and coordinating updates, so you have more brain space for creative and strategic work.
• Automate routine tasks: Agents can take care of repetitive coordination work, like tracking follow-ups, preparing status updates, or keeping tasks and timelines in sync across tools, while you stay involved in review and decisions.

By combining assistants and agents, you get the best of both worlds. The assistant helps you clarify goals and provide feedback, while the agent supports the multistep work behind the scenes, such as research, drafting, and coordination.

Limitations of using AI assistants and agents

While AI agents and assistants are incredibly powerful, they’re not perfect: Agents can add complexity, assistants often lack context, and they both face challenges around accuracy, privacy, and the need for constant oversight. The good news is you can overcome these challenges once you understand them. Let’s take a closer look.

Limitations of AI assistants

AI assistants are great at helping with individual tasks but can struggle with more complex work or multistep workflows, along with other challenges such as:

• Limited autonomy: Assistants typically work through tasks step by step and rely on your guidance to move forward.
• Shallow context: Assistants may not retain context between conversations, which means you may need to reintroduce key details or background information.
• Reactive: Assistants may not be able to proactively jump in to help; they often wait for your guidance, which adds extra steps to your workflow.

Limitations of AI agents

AI agents can proactively jump in to help, but they can make mistakes or veer off course without proper feedback. Let’s take a closer look at these limitations:

• Getting stuck in loops: If agents get stuck, they might repeatedly try the same failed approach or chase unproductive paths.
• Higher cost: Agents can require more computing resources than assistants, especially when handling ongoing or multistep workflows, which can increase costs over time.
• Risk of misalignment: Because agents operate with less step-by-step guidance, they need regular review and feedback to ensure their decisions stay aligned with your goals.

Whether you’re using an assistant or an agent, challenges around data privacy, reliability, and oversight still apply. Understanding these shared limitations can help you use AI tools more effectively.

• Privacy: Both assistants and agents rely on at least some information to be helpful, so it is important to understand how your data is collected, stored, and used by the tools you choose.
• Reliability: AI tools can sometimes produce confident but incorrect information, often referred to as hallucinations. They also don’t always explain how they arrived at an answer, which can make mistakes harder to spot or correct.
• Bias: Because these tools are trained on large datasets of human-generated content, they may reflect existing societal biases, leading to incomplete or skewed outputs.

That’s why the best results come from keeping humans in the loop. Review AI-generated output before publishing it, double-check facts and important details, and ask for sources when something seems unclear.

How to get started with AI assistants and agents

A good way to get started with AI assistants and agents is to start small with AI assistants, then gradually explore agentic tools that support multistep workflows. Here’s a simple, low-risk way to get started:

1. Start with AI assistants to build confidence: If you’re new to AI tools, assistants are an easy entry point. Use them for tasks you already do every day, like drafting emails, formatting citations, or organizing notes, to get a feel for what AI can (and can’t) help with.
2. Layer in agents to automate simple workflows: Once you’re comfortable, look for small, repeatable workflows where an agent could help reduce coordination work. For example, you might use an agent to help track client follow-ups or keep your class notes organized over time.
3. Expand your usage to complex workflows: As you gain experience, you can start applying agents to broader projects, such as organizing research for a final paper or helping manage work across multiple tools and deadlines.
4. Always review results and give feedback: Whether you’re using an assistant or an agent, avoid a “set-it-and-forget-it” approach. Review outputs, double-check important details, and give feedback when something isn’t right. This helps improve results and keeps work aligned with your goals.

Remember, the goal isn’t to remove yourself from the process—it’s to reduce busywork so you can focus on the tasks that require your judgment, creativity, and decision-making.

Choosing the right AI for the job

AI assistants and AI agents aren’t rivals—they work best together. Grammarly shows how the two complement each other in everyday work. Its AI assistance offers instant, in-the-moment help with individual writing tasks, so you stay in control. Its AI agents go further by proactively handling multistep workflows and surfacing context-aware suggestions as you work. Because these agents don’t wait for prompts, they help connect disconnected workstreams and move work forward, saving time and mental energy. Together, they work across the writing tools you already use to help you draft, summarize, and revise content that’s clear, compelling, and authentically yours.

At a broader level, this combination creates a toolkit for modern work: an assistant you can guide with goals and feedback, backed by an AI agent that can carry out the steps needed to achieve those goals. The best way to understand what works is to experiment—start with an assistant for everyday tasks, then try agents for simple, repeatable workflows. Over time, you’ll develop a sense for which tool fits each situation, and that’s when these technologies deliver the most value.

AI agents vs. AI assistants FAQs

How are AI assistants and AI agents different?

The main difference is how much direction they need. AI assistants respond to your prompts and help with tasks one step at a time. AI agents, by contrast, can work toward a broader goal—handling multistep workflows with less ongoing instruction and looping you in for feedback when needed.

Are AI agents smarter than AI assistants?

Not exactly. AI agents aren’t inherently “smarter,” but they’re designed to do more. Both rely on similar underlying AI models, but agents pair those models with capabilities like memory, planning, and integrations across tools. This allows them to manage more complex, multistep work on your behalf.

Can AI assistants become agents?

Yes. AI assistants can take on agent-like behavior when they’re given additional capabilities. To move beyond reactive, one-off responses, they need ways to plan across multiple steps, take action through connected tools and systems, and retain context or feedback over time. With these capabilities, an assistant can help advance a goal rather than respond to individual prompts alone.

How do AI assistants and agents work together in modern tools?

They often work as a pair. Assistants provide a conversational interface for setting goals and giving feedback, while agents operate behind the scenes to coordinate multistep workflows. For example, you might ask an assistant to draft an email, then rely on an agent to schedule follow-ups, track responses, or update related tools automatically.

Does Grammarly use AI agents or assistants?

Grammarly uses both. Its AI assistance responds to your prompts and supports individual writing tasks, while its AI agents proactively help improve your work by offering personalized suggestions, identifying credible sources, and anticipating how readers might respond—without waiting for explicit instructions.

Check out our AI assistant page and AI agent hub to learn more.

Do I need technical skills to use an AI agent?

No. Many everyday tools now include AI agents that work out of the box with no technical setup. That said, more advanced agent platforms, such as OpenAI’s Operator or Anthropic’s Computer Use API, are designed for developers who want to build custom or highly specialized agents.