Agentic AI vs Generative AI: Key Differences

Introduction

When we talk about Agentic AI vs Generative AI, it all comes down to a single question which is, does AI create something or does it get something done on your behalf. Generative AI creates new content whenever you give a prompt to it. It can be text, an image, code, video, or even a summary. On the other hand, Agentic AI’s main purpose is to work towards a goal. You can describe a goal to it and then it does the planning with steps, analyse which tools to use for each step, checks for its own work and keep working until it gets the job done.

Both Agentic AI and Generative AI run on LLMs. The real difference is what sits around the model. Gen AI works is to stop once it answers, whereas Agentic AI works on planning, memory and the power to act inside other software. In this blog, we will discuss Generative AI vs Agentic AI with simple practical examples.

Agentic AI vs Generative AI: The Overview

The following table summarises the main differences between Generative AI and Agentic AI.

Question	Generative AI	Agentic AI
What you give it	A prompt	A goal
What you get back	A draft: text, image, code or audio	A finished task
Who decides the next step	You	The system, within limits you set
How long it works	One reply, a few seconds	Minutes or hours, across many steps
Memory	Mostly the current chat	Tracks progress across steps and sessions
Apps and tools	Optional extras	Core to how it works
Think of it as	A fast writer on call	A junior teammate with a to-do list
When it fails	Wrong facts or bland content	Wrong actions in real systems
How you keep it in check	Read and edit the output	Approvals, access limits and action logs
Example	Drafts a refund email for a late order	Checks the order, issues the refund and emails the customer

Look at the last row. Same problem, very different amount of work taken off your plate.

Now that we have a basic understanding of the basic difference between the two, i.e., Agentic AI vs Generative AI. Lets move on to next section where we explain each in detail with practical examples.

As AI becomes more involved in network operations, engineers who understand both Generative AI and Agentic AI can explore new ways to automate troubleshooting, monitoring, and everyday tasks. Our AI for Network Engineers Course covers Generative AI and Agentic AI concepts in the context of networking.

What Is Generative AI?

Generative AI is a type of artificial intelligence that creates new content based on patterns learned from training data and the instructions or context it receives. Depending on the model, it can generate text, images, code, audio, video and other forms of content.

Generative models learn statistical patterns from large datasets and use those patterns to produce outputs. For example, a language model generates text by predicting likely tokens based on the preceding context.

How Does Generative AI Work?

A typical Generative AI interaction follows these steps:

1. You Provide a Prompt: Enter a question, instruction or description of what you need.

1. The Model Processes the Input: It uses its learned patterns and the available context to interpret the request.

1. The Model Generates an Output: It produces a response, such as a paragraph, image, code snippet or summary.

1. You Review or Refine the Result: You can accept the output, edit it or provide another prompt to improve it.

The process may involve several rounds of interaction, but a basic Generative AI model does not automatically complete an external task just because it has generated a response. Additional tools, integrations or an agentic workflow may be needed to take action.

For example, if you ask a Generative AI application to find flight options from India to Paris, it might suggest an itinerary or summarise available information if it has access to suitable data. Booking a flight, entering passenger details and completing payment require additional capabilities and appropriate authorisation.

What Is Agentic AI?

Agentic AI refers to AI systems designed to pursue a goal by planning and carrying out multiple steps, using tools and evaluating results along the way. Depending on the system, it may operate with limited supervision or require human approval at important stages.

Unlike a simple question-and-answer interaction, an agentic workflow can decide which action to take next based on the task’s progress. Many such systems use LLMs for reasoning and language tasks, but they also rely on software components that manage tools, permissions, state and execution.

How Does Agentic AI Work?

Agentic AI works by understanding a goal, creating a plan, taking action through available tools, and evaluating the results. Unlike a system that only generates a response, an Agentic AI system can complete multi-step tasks with limited human intervention, depending on its permissions and design. 

The process typically involves these five steps: 

1. Goal Understanding: The system interprets the user’s request and identifies the desired outcome. 

1. Planning: It breaks the goal into smaller tasks and determines the steps needed to complete them. 

1. Action: It uses tools, APIs, databases or other connected systems to perform the required tasks. 

1. Monitoring and Feedback: It checks the results, identifies errors or missing information, and adjusts its approach when necessary. 

1. Task Completion: It provides the result or completes the requested workflow. For sensitive actions, such as approving payments, human authorisation may be required. 

Example: If you ask an agent to identify three packaging suppliers that can deliver by Friday and provide a quote. It will look for the options, compare them, send the emails and record them in a spreadsheet. You determine the objective and it takes care of the steps. 

You may have heard the term AI agent. In this case an agent is a single worker, and Agentic AI is the broader architecture to enable teams of agents to work together. 

Key Differences: Gen AI vs Agentic AI

Although Generative AI and Agentic AI can use similar models, they differ in how they handle tasks, make decisions and interact with external systems.

1. Prompts vs Goals

Gen AI is pretty rigid and needs specific instructions or prompts for every output. On the other hand, an agent takes a goal and breaks it down into multiple steps or prompts on its own. So, to some extent, prompting is similar to step-by-step directions and goal setting is like handing over the address.

2. Waiting vs Starting

Gen AI models remain idle until prompted. Agentic AI models are proactive. It can start working on its own based on certain triggers, for example, the arrival of a new support ticket or an order payment failure.

3. Output vs Outcome

For Gen AI, the output is the final words. For an agent, the output is turning in a work product to complete a task. A refund email is the output. A happy, refunded customer is the outcome.

4. Chat Memory vs Working Memory

Like most chatbots, Gen AI and chat GPT have short-term memory. Agents, however, work with a state across several steps, so they know step three failed and step four is no longer needed.

5. Talking vs Doing

Agents can move files, click buttons, update fields, and call APIs. We are starting to see agents play a larger role in clickstream interactions via the Model Context Protocol (MCP) and similar standards. On the other hand, Gen AI only talks.

6. Different Kinds of Risk

With Gen AI, the worst that can happen is you end up with a poorly written paragraph. Not so with agents. There’s a real risk an agent can email the wrong client or clear a record. Databricks calls this informational risk vs. operational risk.

How Generative AI and Agentic AI Work Together?

Generative AI and Agentic AI work together to form a complete system. Most agents use a Generative AI model to read and write. The Agentic AI layer handles getting things done.

Here’s a simplified version of a common content marketing workflow:

1. Set a Goal: You set a goal for your agent to publish an SEO blog weekly using your target keywords.

1. Gather Information: Your agent pulls your target keyword data for the week and determines a topic.

1. Generate a Draft: Your agent asks a Generative AI model to write an outline and the first draft.

1. Review the Content: The agent uses your brand rules to determine if the draft is on-brand and sends it to an editor.

1. Prepare Publication: The agent publishes the blog in your CMS, schedules it to post and notifies your team

Generative AI did one thing, writing the blog, while the agent did the rest. Without Generative AI, the agent can’t do step 3, and without the agent, you would have a blog draft that wouldn’t be published.

Which One Should You Use: Agentic AI vs Generative AI

Choose the approach based on the task, the level of automation required and the risks involved, rather than assuming one is always better.

Go with Generative AI when:

1. You need a draft, summary or idea fast

1. A person will review and act on the result anyway

1. The task is done in one or two steps

1. Mistakes are cheap and easy to fix

Go with Agentic AI when:

1. The work repeats often and follows clear rules

1. It spans several apps, like your CRM, inbox and a spreadsheet

1. You can clearly define what “done” looks like

A few real pairings by team:

1. Marketing: Gen AI writes ad variations. An agent runs the A/B test and shifts budget to the winner.

1. Software: Gen AI suggests code. A coding agent writes the fix and runs tests, then opens a pull request.

1. Security: Gen AI explains an alert. An agent investigates and isolates the infected laptop.

1. HR: Gen AI writes the job post. An agent reviews applicants and schedules interviews.

Risks and Limits to Keep in Mind

Generative AI can fill in facts that it thinks are correct, can be biased and repetitive from its training data, and can create generic and vague content.

Agentic AI carries these risks, and adds its own:

1. Errors from previous steps can compound
2. It needs access to real world systems, raising security and privacy risks
3. Each step requires a model call, so longer processes are more expensive
4. It is harder to determine why the AI took a given action

There is a lot of hype around Agentic AI. However, Gartner estimates that 40% or more of Agentic AI projects will be abandoned within the next 5 years due to rising costs, lack of clear value, and weak risk controls. It also discusses “agent washing,” where vendors label ordinary chatbots as Agentic AI.

Always keep it simple and human approved for big and/or irreversible tasks. Log every single action it takes and only allow access it truly needs.

Conclusion

Understanding Agentic AI vs Generative AI helps businesses choose the right approach for their needs. Generative AI is useful for creating content, summarising information and assisting with tasks, while Agentic AI can coordinate multiple steps and use tools to work towards a defined goal. Many organisations can benefit from combining both. Start with a clear use case, measure the results and maintain appropriate human oversight, especially when AI can take actions in real systems.

Frequently Asked Questions

Q1. Is ChatGPT Generative AI or Agentic AI?

ChatGPT can be used for Generative AI tasks, such as answering questions and creating content. Depending on the features and tools available, it can also support agentic workflows that carry out multi-step tasks. Whether a particular interaction is agentic depends on the capabilities being used.

Q2. Is Agentic AI Better Than Generative AI?

Neither is universally better. Generative AI is often suitable for content creation and information assistance, while agentic AI is useful for workflows that require multiple steps, tool use and progress tracking. The best choice depends on the task, cost, risks and level of oversight required.

Q3. Can Agentic AI Work Without Generative AI?

Yes. An agentic system can use predefined rules, conventional software, search tools or other models to pursue a goal. However, many modern agentic systems use generative models, including LLMs, to interpret instructions, reason through tasks and produce content.