These AI models and systems rely on advanced machine learning and neural networks to analyze and replicate data patterns they are trained on.

While genAI can optimize creativity and content creation, its outputs are only as good as the data it’s trained on. If you feed genAI flawed, biased, or incomplete data inputs, its outputs will reflect those. Also, genAI can only predict what comes next based on the patterns it has seen or analyzed before.

Despite its flaws, generative AI is quickly transforming, optimizing, and reshaping creative industries—from music, entertainment, to design and marketing.

What is Agentic AI?

Where generative AI creates, Agentic AI acts. This AI model is designed to make decisions autonomously and solve complex tasks, most notably in a customer service and support role.

Agentic AI systems are designed to autonomously make decisions, solve problems, improve, and adapt to dynamic and evolving environments.

What sets agentic AI models apart is their ability to act with purpose. They don’t just react to inputs, they can consider objectives, decipher context, and make choices accordingly to achieve these requests.

For example, an autonomous or self-driving vehicle uses advanced software and hardware to navigate routes without human input. It uses sensors and cameras to perceive its surroundings, collect data, and process information through complex algorithms and machine learning models to make real-time decisions, such as detecting roadside obstacles and traffic conditions to determine the best path, all without human intervention.

However, the advanced autonomy of agentic AI also poses ethical questions: What happens if an autonomous AI model system makes a mistake? Who is responsible for the potential result or fall-out? How do we, as humans, ensure these AI systems align with human values? As agentic AI becomes more prevalent, we must answer these questions and challenges.

Comparison table: Agentic AI vs. Generative AI

Component	Agentic AI	Generative AI
Purpose	Automates decision-making and complex reasoning	Content generation – text, images, code
Function	Completes and execute tasks by understanding goals and context	Generates outputs based on patterns in training inputs and data
Strength	Autonomously handles complex, logic-based, and multi-step workflows	Produces creative, human-like outputs in text, images, code, etc.
Use case	Adaptive problem-solving, self-learning and improvement, and workflow automation capabilities	Content creation – brainstorming, writing, art
Examples	Autonomous troubleshooting system errors and customer support workflows, self-driving cars	Writing articles, social media copy, advertisements, designing graphics, coding prototypes

How Agentic AI and Generative AI work together

Despite their key differences, generative AI and agentic AI can still work well together.

For example, an AI virtual assistant can benefit from agentic AI handling real-time customer interaction, such as receiving and answering inquiries, guiding the conversation, and making decisions based on user input. Then, generative AI can create nuanced responses to specific questions and steer the conversation accordingly.

Generative AI use cases

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