Generative AI vs. Agentic AI: What's the Difference?

Since OpenAI launched its flagship tool, ChatGPT, in late 2022, generative AI has become a common tool for many workers. With its ability to draft emails, summarize documents, write code, brainstorm ideas, and more, workers quickly adopted ChatGPT because it fit naturally into their existing workflows.

Now, workers aren’t just using AI to generate outputs. Many are building their own AI agents that perform tasks, run autonomously, and operate inside company systems without a human overseeing or prompting every step. That’s a massive leap from the reactive capabilities of generative AI, and most people still haven’t fully reckoned with what that leap means.

According to a recent Clutch survey, 64% of full-time workers have already taken a stab at creating their own AI agent, and another 9% plan to do so. Of those who’ve tried, 95% have succeeded, with 71% accomplishing the task in under a week.

"Not long ago, building an agent meant pulling in a dev team,” says Marcin Sulikowski, Co-CEO of web development and design agency Naturaily. “Now someone in sales or support can stand one up to solve their own bottleneck."

If workers are already building agents at this scale, the obvious questions follow: What exactly is an AI agent? How is this type of AI different from the generative AI tools we’ve all been using for years? Most importantly, what does this mean for how we get work done and how organizations should respond?

We’ll explain what generative AI and agentic AI are, how they’re used, and what distinguishes them. We’ll also look at how agentic AI works in practice and what businesses and workers should consider before moving from generative AI to agentic AI.

What Is Generative AI?

Generative AI takes an input, like a prompt, an image, or a document, and creates new content, such as text, images, audio, video, and code. It does this by predicting what should come next based on patterns it learned from existing data, essentially synthesizing its training data to answer the user’s request.

By nature, generative AI is reactive. It waits for a prompt, responds, and stops. Unless prompted again, it doesn’t take follow-up actions. It doesn’t retain persistent memory of a task across sessions unless its developers specifically designed it to do so.

Some examples of generative AI at work include:

• Asking ChatGPT to draft an email about a particular subject
• Uploading a document to Claude and asking it to summarize the information within
• Prompting Midjourney to create an image with specific parameters

In each of these examples, the AI receives a prompt, generates a response, and waits for the next human input before taking any further action.

Where Generative AI Excels

Generative AI is best for tasks involving:

• Text and content creation: Writing drafts, emails, creative pieces, and marketing copy
• Data extraction and cleaning: Finding specific entities (like names or locations) within messy text blocks
• Summarization: Condensing long articles, transcripts, or reports into brief overviews
• Coding and formulas: Writing, debugging, and explaining programming scripts or spreadsheet formulas
• Translation: Translating to and from various languages, adapting tone, preserving idioms, understanding industry-specific context, and handling regional dialects
• Ideation: Brainstorming creative angles, product names, or alternative problem-solving paths

Any task in which the output is the deliverable and a human subsequently reviews or acts on it is a potential use case for generative AI.

What Is Agentic AI?

Agentic AI autonomously executes multiple steps to achieve a defined objective, using tools, reasoning through problems, and adjusting its approach along the way with minimal human supervision.

Generative AI can produce an answer when given a prompt. An AI agent goes further by determining what needs to happen next and taking action to advance a task. Rather than simply generating a response, an agent can interpret an objective, break it into steps, use external tools or information, evaluate what happened, and decide what to do next.

For example, imagine asking an AI agent to research potential vendors for a software project. A generative AI tool could summarize information from the material you provide or compile information it finds online.

In contrast, an agent could search for vendors, gather information from multiple sources, compare them against your criteria, organize the findings, and produce a shortlist. If it encounters missing information or an unexpected result, it can adjust its approach rather than stopping after its first response.

How Do You Build an AI Agent?

Creating an AI agent doesn't necessarily mean building an AI system from scratch. Depending on the tool, users can create an agent by describing what they want it to accomplish and configuring the instructions, tools, data, and permissions it needs to complete the task.

A typical setup involves several steps:

1. Define the objective: Start by identifying what the agent should accomplish. This might be something broad, such as qualifying sales leads, or a repeatable workflow, such as turning research into a first draft.
2. Give the agent instructions: Users establish what the agent should do, how it should approach the task, what rules it should follow, and what a successful outcome looks like.
3. Connect tools and data: The agent may need access to applications, APIs, databases, websites, files, or other systems to gather information or take action. The user determines which tools and data sources the agent can access.
4. Set permissions and boundaries: Users can determine which actions an agent is allowed to take independently and which require human approval. For example, an agent might be allowed to draft an email but require approval before sending it.
5. Test and refine: Users run the agent, review its results, identify where it makes mistakes or gets stuck, and adjust its instructions, tools, or workflow.

The amount of technical knowledge required varies considerably. Some platforms provide visual interfaces and prebuilt integrations that allow non-developers to configure an agent without writing code. Others provide coding environments that give developers more control over the agent's logic, tools, and behavior.

How AI Agents Work

Once configured, an agent typically operates on a recurring basis rather than following a fixed sequence of instructions from a person.

• Understand the objective: The agent interprets the request and determines what needs to be accomplished.
• Plan the work: It identifies the steps, information, and tools needed to complete the task.
• Take action: The agent uses available tools, such as APIs, databases, web browsers, software applications, or internal systems, to carry out those steps.
• Evaluate the results: It reviews the information or outcome of each action to determine whether it achieved what was expected.
• Adjust and continue: If the result is incomplete or doesn't move the task forward, the agent can change its approach and take another action.
• Complete the objective: Once the necessary steps are finished, the agent returns the result or takes the final action.

This loop is what gives agentic AI its autonomy. The agent doesn't need a person to provide a new prompt after every step. Instead, it can use the results of one action to determine what to do next.

Agentic AI in Practice

The difference between generative and agentic AI becomes clearer when you look at what happens after the initial request.

Consider a marketing employee who wants to turn a new piece of research into a campaign. With generative AI, the employee might ask for a blog outline, social posts, or an email draft and then decide what to do with each output. An AI agent could coordinate more of that workflow itself. It might review the research, identify relevant findings, draft campaign assets, check them against predefined requirements, organize the files, and send the completed materials for review.

The agent still operates within the permissions, tools, and instructions it has been given. But instead of requiring the worker to direct every individual action, it can move between steps and respond to the results along the way.

Clutch's research shows how workers are already using this capability. Among workers who have built their own AI agents, 83% use them to write, edit, or summarize content, 64% for research, and 56% for data entry and processing.

The Key Differences Between Generative AI and Agentic AI

Examining generative AI vs. agentic AI reveals differences in multiple areas, which we’ve summarized in this simple comparison table.

An important note about agentic AI is that its autonomous nature introduces additional risk that generative AI doesn’t pose. We’ll cover this in more depth near the end of this piece.

How This Changes the Way People Work

Generative AI and agentic AI affect workflows, output, and daily work differently.

AI agents handle the repetitive, multi-step tasks that used to chew up hours of valuable working time. Better still, the technology performs these tasks without waiting for the worker to prompt it each time.

Clutch’s data show that 90% of workers who built AI agents save several hours each week. Eighty percent complete tasks faster, and 39% report gaining more time for important strategic work once agentic AI takes the mundane, repetitive tasks off their plates. In fact, 78% of workers who built their own AI agents did so specifically to automate repetitive tasks.

These aren’t just IT professionals or developers, either. Among workers who build AI agents, 57% are individual contributors, and 54% hold non-technical roles. By department, marketing (29%) and IT/operations (32%) lead the agent-building charge

Learn more about How Marketers Are Using AI Agents.

While building, 89% took an experimental, learn-as-you-go approach. This tells us that these people are largely workers solving their own bottlenecks rather than formal organizational deployments.

While generative AI accelerates individual tasks, agentic AI fully automates entire sequences of tasks and can run in the background while the worker performs a completely different, unrelated task. Agentic AI provides next-level time savings.

What To Consider Before Going Further

Upgrading from generative to agentic AI entails various risks and governance considerations. The main risk of using agentic AI doesn’t exist with generative AI: Mistakes happen before a human can catch them. With generative AI, human oversight is a given. A person reads the draft before it goes out.

An AI agent, on the other hand, might send the email before anyone notices the error.

“Building a working agent prototype is easy now,” says Hammad Maqbool, AI Lead at AI-first digital engineering partner Phaedra Solutions. “Making it reliable, secure, and maintainable once it's in production is the hard part.”

Ninety-five percent of workers who built agents report encountering problems. Their agents:

• Generated inaccurate or misleading information (70%)
• Sent unwanted communications (49%)
• Deleted or modified unintended items (42%)
• Exposed or mishandled sensitive data (11%)

Nate Botelho, Founder of award-winning web development company Temper And Forge, says, "When an AI agent sends something externally that it shouldn't have, the fallout can extend well beyond an embarrassing email."

This is where poor AI governance can negatively impact your team. Most workers are building AI agents without formal oversight, and companies only catch onto issues after an agent makes a mistake. Although 90% of employers ask their workers to scale or document the agents they build, those guardrails tend to come after they have already experimented, not before.

“Companies do not need a perfect governance framework before employees can experiment,” says Igor Epshteyn, CEO at custom software development and consulting company Coherent Solutions, “but they do need a few critical guardrails from the beginning.”

Before allowing a worker to experiment with creating their own AI agents, your organization should ask these essential governance questions:

1. What data will this agent access, and does it need to access it to operate?
2. What actions can this agent take autonomously, and what requires human approval?
3. How will you catch and correct errors before they compound?
4. Who owns the agent once you build it: the individual who built it, or the team or organization?

Putting guardrails in place ahead of time as part of your company’s AI strategy can help prevent costly reputational damage, severe security breaches, and the unpredictable risks posed by misaligned AI behavior.

Learn more about Agentic AI and How It’s Transforming Business.

Using Agentic AI Safely and Responsibly

Generative AI changed how workers create content and other media assets, while generative AI extends that shift by automating entire workflows and operating autonomously within company systems.

But there’s a reason for workers to use both within their workflows. Workers can use generative AI for tasks such as writing, brainstorming, analysis, and communication, while agentic AI handles more complex, multi-step processes. Together, this can help workers become more efficient and effective.

Learn more about How to Set Up Your Business with AI: From Basics to AI Agents.

About the Author

Hannah Hicklen Content Marketing Manager at Clutch

Hannah Hicklen is a content marketing manager who focuses on creating newsworthy content around tech services, such as software and web development, AI, and cybersecurity. With a background in SEO and editorial content, she now specializes in creating multi-channel marketing strategies that drive engagement, build brand authority, and generate high-quality leads. Hannah leverages data-driven insights and industry trends to craft compelling narratives that resonate with technical and non-technical audiences alike. 

See full profile