Agentic AI VS Generative Artificial Intelligence Solutions – What’s the Difference?

The world is changing, especially with the introduction of artificial intelligence solutions. Generative AI technology is something that the public as well as businesses are using currently. Another technology that is causing a huge wave, especially in the corporate world is agentic AI. For any person or organization aiming to use artificial intelligence to its full potential, it is important to understand the distinction between the technologies to determine which one is better applied to specific use cases.

In this post, we will be discussing the difference between generative AI and agentic AI in detail and expand upon the use cases to help for added clarity.

What is Agentic AI?

Agentic artificial intelligence solutions are dedicated to doing activities. It is the type of AI-powered solution created to act autonomously to achieve specific goals.

Agentic AI solutions are the tools responsible for making decisions, taking actions, and evolving as per the changing environments.

This is the brain behind autonomous vehicles, robotic process automation, or smart assistants. They are capable of scheduling meetings, price optimization, or shopping for groceries. As they require information to make autonomous decisions, these systems are generally equipped with sensors and algorithms to understand their environment, process information, and execute actions.

It is different from other technologies as it acts with purpose. Instead of reacting to an input, it is taking goals into consideration and making choices to reach them.

What are the Core Technologies Behind Generative AI?

LLM-Powered Frameworks: LangChain, OpenAgents

Reinforcement Learning (RL): Allows agents to learn from feedback.

Planning + Tool Use: Enables access to web search, APIs, and data stores.

Examples: AutoGPT, AI assistants in enterprises

What is Generative AI?

Generative artificial intelligence solutions are the tool of creation. Much like a human, it draws inspiration from what already exists to create something entirely new, be it in the form of text, images, music, code, or video. Made to mimic human creativity, it is a tool that is showing potential use cases in a variety of industries.

Now, almost all internet users are using artificial intelligence solutions in one way or the other. Businesses are using ChatGPT, Midjourney, and several other generative AI tools to make operations easier.

However, it is trained on data, and therefore, biased or incomplete information can lead to flawed results.

What are the Core Technologies Behind Generative AI?

Transformers: GPT-4, PaLM, Claude

Diffusion Models: Used for image generation, in tools like DALL-E or Midjourney

Large Language Models (LLMs): Models trained to predict next token in the sequence.

Examples: ChatGPT, DALL-E 3, GitHub Copilot

What is the Difference between Agentic AI & Generative AI?

While both generative AI development solutions and agentic AI services come under the banner of AI, they are vastly different in their applications.

Aspect	Agentic AI	Generative AI
Definition	AI systems designed to autonomously perform tasks, make decisions, and interact with environments to achieve specific goals.	AI systems focused on creating new content, such as text, images, audio, or code, based on patterns learned from training data.
Primary Function	Executes goal-oriented actions, often involving planning, reasoning, and interaction with external systems or users.	Generates creative or predictive outputs, such as writing, art, or data synthesis, based on input prompts or datasets.
Autonomy	High autonomy; can independently plan, adapt, and execute tasks with minimal human intervention.	Limited autonomy; primarily responds to prompts and requires human guidance for task direction or refinement.
Decision-Making	Capable of complex decision-making, often using reasoning, contextual analysis, and real-time data to achieve objectives.	Minimal decision-making; focuses on producing outputs based on learned patterns without evaluating broader goals.
Interaction with Environment	Actively interacts with external systems, APIs, or environments to gather data, perform actions, or achieve outcomes.	Typically operates in isolation, producing outputs without direct interaction with external systems or environments.
Examples of Tasks	Automating workflows, managing schedules, optimizing logistics, or controlling robotic systems.	Writing articles, generating images, composing music, or summarizing text.
Output Type	Actions, decisions, or task completions (e.g., booking a meeting, optimizing a supply chain).	Creative or synthesized content (e.g., a poem, a generated image, or a chatbot response).
Learning Approach	Often combines reinforcement learning, planning algorithms, and real-time adaptation to environments.	Primarily relies on supervised or unsupervised learning, such as transformers trained on large datasets.
Use Cases	Autonomous vehicles, intelligent virtual assistants, automated customer support systems, or smart home management.	Content creation tools (e.g., ChatGPT, DALL-E), code generation (e.g., GitHub Copilot), or synthetic data generation.
Human Involvement	Minimal after initial setup; designed to operate independently with predefined goals.	High during operation; requires prompts, feedback, or curation to guide and refine outputs.
Error Handling	Can adapt to errors by re-planning or adjusting actions based on real-time feedback from the environment.	Errors often require human intervention to correct or refine the generated output.
Complexity of Goals	Handles complex, multi-step goals requiring coordination and long-term planning.	Focused on single-step or short-term creative tasks, with less emphasis on long-term objectives.

Where Do Agentic AI and Generative AI Intersect?

With all the differences highlighted, it might appear that generative AI and agentic AI do not go together. However, that is far from the truth. In contrast, the two technologies working together can be among the more powerful applications of AI solutions.

For example, agentic AI solutions could leverage generative AI solutions for more effective communication. Combined, these tools can do wonders, especially in customer service.

Other areas where the two technologies come together are robotics, healthcare, and more. The true extent of the potential of generative AI and agentic AI working together is still in the process of exploration.

Give Your Business the Push It Needs with Artificial Intelligence Solutions

Businesses that want to automate parts of their processes using intelligent solutions should invest in agentic AI solutions. Generative AI technology is the tool you choose to improve the overall creativity of your employees. Combined together, these two technologies can make a huge impact on your business.

If you would like to know how to leverage the two technologies together, get in touch for the best AI/ML development services and bring your vision to life.

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