Agentic AI vs. generative AI: What's the difference?

Agentic AI vs. generative AI

Generative AI creates user-requested output using desired elements and styles reflected in its training data. For example, a generative AI model specializing in image generation might receive a user query to recreate a family photo into a cartoon.

A generative AI model can only create what the user requests, translating prompts into processed output. In addition, generative AI is designed to synthesize rather than analyze, so it is not well-suited for analytics such as strategic business planning. However, it is particularly useful for content creation, data augmentation and personalization.

Generative AI tools often focus on specific media such as text, music, images, video and code. Examples include the following:

• AI chatbots, such as OpenAI's ChatGPT and Anthropic's Claude.
• Image generators, such as Midjourney and OpenAI's DALL-E.
• Audio generators, such as Google's Lyria.
• Video generators, such as OpenAI's Sora.
• Code creation tools, such as GitHub Copilot.

In contrast, agentic AI acts autonomously using data, well-defined rules and clear workflows. AI agents make complex decisions and take actions to achieve desired outcomes with a high degree of independence, requiring little -- if any -- human control.

AI agents understand data context, exhibit reasoning capabilities and adapt to changing environments. This makes agentic AI well-suited for a variety of real-world use cases, such as handling complex workflows, assisting customers or aiding supply chain management.

Agentic AI is typically reactive or proactive:

• Reactive. AI agents process data and then respond in accordance with predefined rules. They are simple, fast and readily predictable systems.
• Proactive. AI agents receive predefined goals, and can then reason, adapt and plan iteratively to achieve the intended outcome. Success is defined by how closely the actual outcome matches the intended outcome.

How does generative AI work?

Generative AI architecture includes various models and techniques, including machine learning (ML) algorithms called deep learning models. These algorithms are built with neural networks that use multiple layers to analyze and learn from data.

Deep learning models simulate how the human brain learns and makes decisions. They can handle unstructured data such as images, text and audio. Common types of deep learning models include recurrent neural networks (RNNs), convolutional neural networks (CNNs) and transformers, which power most large language models (LLMs) -- a prominent form of generative AI that focuses on understanding and creating human language.

Generative AI models train on enormous amounts of data, perhaps the complete literary works of Shakespeare or the paintings of impressionist artists. Models identify and encode the relationships and patterns of the training data, learning the data's relevant elements or attributes.

Human users employ generative AI by creating a prompt -- a request that specifies the desired outcome along with associated attributes. Prompts can be highly sophisticated and sensitive, and the practice of prompt engineering can be a dedicated job role. A simple example of an image prompt might be: "Create a watercolor painting of a spooky forest in the style of Miyazaki."

The generative AI model then draws from its learned data to synthesize an output that addresses as many elements of the prompt as possible. Users can expand, refine and add details to the prompt to elicit a more desirable output.

However, generative AI cannot create from scratch. It can only draw from the data that it has learned. For example, if a user requests an explanation of a Python code segment, but the generative AI model has not been trained in Python, then it might be unable to provide accurate output.

How does agentic AI work?

Agentic AI architecture involves some of the same models and techniques found in many forms of generative AI, such as LLMs. As with other deep learning models, LLMs train on enormous data sets, but their use of natural language processing (NLP) enables them to capture complex language patterns, glean context and understand the meaning or intent behind language.

LLMs combine with ML training methods like reinforcement learning and other technologies to build agentic systems with high levels of understanding. This fundamental capability enables AI agents to operate autonomously.

An agentic AI system operates in four phases: collect, reason, act and learn.

• Collect. The agent brings together data from across the enterprise, such as data from fleets of IoT devices or corporate databases.
• Reason. The AI agent processes the data to analyze and reason, enabling it to understand the situation.
• Act. The AI agent can then plan and act, making choices and taking actions across environments to meet the user's needs or stated goals.
• Learn. Because agentic AI is iterative, the agent relies on its memory and outcome evaluation to measure effectiveness and continuously improve through dynamic learning.

Using agentic AI and generative AI together

When used together, agentic AI and generative AI can provide more creative and powerful capabilities than either technology can offer individually. Generative AI understands natural language and generates content, while agentic AI plans and takes actions that work toward goals.

Blending agentic AI and generative AI can take countless forms, but common examples include the following:

• Content generation and deployment. Generative AI tools can create desired content, such as a code update, and AI agents can handle testing, validation, deployment and optimization of the code release in production.
• Customer service automation. Generative AI tools can draft responses to customer queries, while AI agents can handle customer interactions and needs, such as scheduling appointments or ordering products.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.