{"id":134661,"date":"2026-07-04T18:33:44","date_gmt":"2026-07-04T18:33:44","guid":{"rendered":"\/in\/tutorials\/agentic-ai-vs-generative-ai"},"modified":"2026-08-17T19:16:54","modified_gmt":"2026-08-17T19:16:54","slug":"agentic-ai-vs-generative-ai","status":"publish","type":"post","link":"\/in\/tutorials\/agentic-ai-vs-generative-ai\/","title":{"rendered":"Agentic AI vs. generative AI"},"content":{"rendered":"

Generative AI creates content, while agentic AI completes tasks.<\/strong> <\/p>

Generative AI responds to prompts by producing text, images, code, audio, or other content.<\/p>

Agentic AI goes further by planning how to achieve a goal, using external tools, making decisions throughout the workflow, and adapting its actions until the task is complete.<\/p>

Agentic AI builds on generative AI. Most agentic AI systems rely on large language models to understand requests and generate content, then combine those capabilities with planning, memory, and tool use to complete multi-step workflows.<\/p>

What is agentic AI?<\/strong><\/h2>

Agentic AI refers to AI systems that can complete tasks with minimal human input.<\/strong> <\/p>

After receiving a goal, the system creates a plan, uses the tools and information it needs, checks the results of each step, and continues working until it completes the task.<\/p>

Suppose a customer reports that they can’t access their account.<\/p>

An agentic AI system verifies the customer’s identity, checks recent login attempts, resets the password if appropriate, updates the support ticket, and confirms that the issue has been resolved.<\/p>

The user provides the goal, while the AI determines and completes the necessary steps.<\/p>

Most agentic AI<\/a> systems combine a large language model (LLM) with memory and external tools. <\/p>

The language model interprets the request and decides what to do next, memory keeps track of relevant context, and tools let the system search the web, retrieve documents, run code, or interact with other software.<\/p>

LLM agents are one of the most common types of agentic AI. They power applications such as coding assistants, research tools, customer support systems, and workflow automation platforms by using language models to understand requests and interact with other software.<\/p>

Businesses that want these capabilities without assembling the pieces themselves can use an agentic AI platform<\/a> that provides ready-to-run agents.<\/p>

What is generative AI?<\/strong><\/h2>

Generative AI is a type of artificial intelligence that creates new content from a user’s prompt.<\/strong> <\/p>

It learns patterns from large datasets during training and uses that knowledge to generate text, images, code, audio, video, and other types of content.<\/p>

Generative AI produces an output based on the instructions it receives, but it doesn’t work toward a broader objective on its own.<\/p>

Each response depends on the user’s prompt, and the model waits for the next instruction before continuing.<\/p>

Popular examples of generative AI include ChatGPT<\/strong>, Claude<\/strong>, and Gemini<\/strong> for text generation, image generation tools such as Midjourney<\/strong> and DALL·E<\/strong>, and coding assistants like GitHub Copilot<\/strong>.<\/p>

Agentic AI vs. generative AI<\/strong><\/h2>

The biggest difference between generative AI and agentic AI is their purpose. Generative AI creates content in response to prompts, while agentic AI uses AI to plan, make decisions, and complete tasks with minimal human input.<\/p>

Agentic AI vs. generative AI quick comparison:<\/p>

Feature<\/strong><\/td>Generative AI<\/strong><\/td>Agentic AI<\/strong><\/td><\/tr>
Primary purpose<\/strong><\/td>Create content<\/td>Complete tasks<\/td><\/tr>
User interaction<\/strong><\/td>Responds to prompts<\/td>Works toward a goal<\/td><\/tr>
Decision-making<\/strong><\/td>Generates a response based on the current prompt<\/td>Decides what actions to take throughout the workflow<\/td><\/tr>
Planning<\/strong><\/td>No<\/td>Yes<\/td><\/tr>
Tool use<\/strong><\/td>Optional<\/td>Core capability<\/td><\/tr>
Memory<\/strong><\/td>Uses the current conversation as context<\/td>Uses working and long-term memory to retain relevant information<\/td><\/tr>
Output<\/strong><\/td>Text, images, code, audio, and video<\/td>Completed workflows, actions, and decisions<\/td><\/tr>
Human involvement<\/strong><\/td>Requires prompts for each step<\/td>Operates with minimal guidance after receiving an objective<\/td><\/tr>
Examples<\/strong><\/td>ChatGPT, Claude, Gemini, Midjourney<\/td>Coding agents, research agents, customer support agents, AI assistants<\/td><\/tr><\/tbody><\/table><\/figure>

Generative AI and agentic AI are closely related, which is why the terms are frequently confused.<\/p>

Most agentic AI systems rely on generative AI, usually a large language model<\/a>, to understand requests, reason about the task, and generate content when needed.<\/p>

The difference is what happens after the model produces an answer. Generative AI stops after generating an output, while agentic AI continues working by deciding what to do next, retrieving information, using external tools, and evaluating the results until the objective is complete.<\/p>

In short, generative AI is reactive, while agentic AI is proactive.<\/p>

How generative AI and agentic AI work together<\/strong><\/h2>

Many modern AI applications combine generative AI and agentic AI.<\/p>

Generative AI creates content, while agentic AI decides when that content is needed, how it fits into the overall workflow, and what actions to take next.<\/p>

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