What’s the difference between agentic AI and generative AI?
Two types of AI are dominating the conversation around artificial intelligence. That could change tomorrow at the rate AI is moving. The post What’s the difference between agentic AI and generative AI? appeared first on MarTech.


In MarTech’s “MarTechBot explains it all” feature, we pose a question about marketing to our very own MarTechBot, which is trained on the MarTech website archives and has access to the broader internet.
Q: What’s the difference between agentic AI and generative AI?
Agentic AI represents a significant evolution in the realm of artificial intelligence, particularly in its application within marketing and other business functions.
Here are the key capabilities and use cases for agentic AI, along with a comparison to traditional generative AI tools:
Capabilities of agentic AI
Autonomy: Agentic AI systems can operate independently, making decisions based on data analysis and predefined goals. They can plan steps, execute tasks and self-correct errors without human intervention.
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Goal setting and reasoning: These systems can analyze complex data sets, set objectives and reason through various scenarios to determine the best course of action.
Multi-functionality: Agentic AI can integrate various AI techniques and models, allowing it to perform a wide range of tasks across different business functions, from customer service to supply chain management.
Continuous learning: They can learn from interactions and outcomes, improving their performance over time through machine learning techniques.
Complex problem solving: Agentic AI is designed to handle high-complexity use cases, making it suitable for industries that require sophisticated decision-making processes.
Use cases for agentic AI
Customer support automation: Agentic AI can manage customer inquiries autonomously, providing personalized responses and resolving issues without human intervention.
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Marketing campaign optimization: These systems can analyze campaign performance in real-time, adjusting strategies based on data insights to maximize ROI.
Sales forecasting: By analyzing market trends and customer behavior, agentic AI can predict sales outcomes and help businesses make informed decisions.
Supply chain management: Agentic AI can optimize logistics and inventory management by analyzing data and making autonomous adjustments to operations.
Personalized content creation: While generative AI can create content, agentic AI can tailor that content based on user interactions and preferences, enhancing engagement.
Differences between agentic AI and generative AI tools
Level of autonomy: Traditional generative AI tools, such as those used for content creation (e.g., ChatGPT), typically require human input and oversight. In contrast, agentic AI operates with a higher degree of autonomy, making decisions and executing tasks independently.
Complexity of tasks: Generative AI is primarily focused on producing content based on prompts, while agentic AI can handle complex, multi-step processes that require reasoning and strategic planning.
Integration of functions: Agentic AI can integrate various functions and data sources to perform tasks across different business areas, whereas generative AI tools are often limited to specific applications, such as text or image generation.
Learning and adaptation: While generative AI can learn from data, agentic AI is designed to continuously adapt and improve its performance based on real-time feedback and outcomes.
In summary, agentic AI represents a more advanced and autonomous approach to AI applications in marketing and other sectors, enabling organizations to tackle complex challenges and optimize operations more effectively than traditional generative AI tools.
The post What’s the difference between agentic AI and generative AI? appeared first on MarTech.
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