What is generative AI?
Generative AI is a category of AI focused on creating content based on training data. It uses machine learning models to learn patterns, styles, and structures from the data, and then generates new text, images, code, and other data types that closely resemble human creations.
Basically, it uses data to predict what the next word, sound, or pixel would be in a pattern and NLP to create new content that's based on learned patterns and contexts.
Generative AI was designed to produce new, original content and ideas, often surprising its human counterparts with unexpected and innovative outputs.
Agentic AI meaning
Agentic AI is goal-oriented artificial intelligence that can use predictive models for simple reasoning, make decisions, and take action autonomously while adapting to changing conditions without constant human input.
Unlike generative AI, agentic AI can act on its own and goes a step further from simply generating outputs. It combines planning, memory, and the ability to use tools to understand context, learn from previous outcomes, and determine how to achieve goals. This means AI can now tackle bigger, more complex business problems and even run entire processes.
For IT leaders, agentic AI opens the door to more adaptive and intelligent systems. It allows teams to automate high-value activities instead of repetitive ones, bringing a new level of flexibility and responsiveness to digital operations.
Agentic AI vs generative AI
As enterprise AI evolves, it’s fundamental to understand the difference between generative AI and agentic AI to maximize business.
| Generative AI | Agentic AI | |
|---|---|---|
|
Core capabilities |
Creates new content, code, and solutions from natural language prompts and patterns. |
Drives towards goals by making decisions and acting autonomously. |
|
Adaptation & learning |
Generates responses based on training data, with limited real-time adaptation. |
Continuously adapts based on outcomes, feedback, and evolving context. |
|
Functionality |
Produces outputs like text, code, or images from user inputs. |
Coordinates multi-step tasks to deliver end-to-end outcomes. |
|
Flexibility |
Handles diverse creative tasks. |
Adjusts to shifting conditions, business rules, and operational complexity. |
|
Efficiency |
Speeds up content creation and initial development phases. |
Streamlines complex workflows to accelerate value delivery. |
|
Governance and securitys |
Requires oversight for quality, accuracy, and intellectual property concerns. |
Requires strong governance to ensure safe, responsible, and transparent behavior. |
|
Strategic value |
Democratizes creation and reduces time-to-first-draft across disciplines. |
Unlocks transformative gains in agility, scale, and decision intelligence. |
|
Limitations |
Can produce inaccurate or inconsistent results without human review. |
More powerful, but it introduces complexity that must be governed effectively. |
Having explored the capabilities of agentic AI compared to GenAI systems, it’s time to acknowledge another transformative force, the synergy of generative AI and agentic AI.
Generative AI vs Agentic AI: Unpacking the synergy
The connection between generative AI and agentic AI isn't always clear. Are these competing paradigms vying for your investment, or are they complementary technologies that can amplify each other's strengths?
If you’re an IT leader who is feeling the pressure to build intelligent enterprise solutions, it's essential to understand how generative AI fits in, particularly with the autonomous, goal-oriented capabilities of agentic AI.
How is agentic AI different from generative AI?
With the evolution of AI, the focus has shifted to goal-directed autonomy. Generative AI boosts productivity by allowing the creation of natural language, images, and code from simple prompts; the next step is agentic AI. Agentic AI reduces the amount of prompting needed to get tasks done. For example, if you ask an AI assistant or virtual agent the time slots available for you and your team to meet, once it finds a time, it asks you if you’d like to schedule it. With agentic AI, it would schedule the meeting on its own automatically.
By combining autonomy, natural language understanding, memory, and goal-directed behavior, agentic AI delivers intelligent automation that’s adaptable, governed, and aligned with business outcomes.
Agentic AI vs Generative AI: Common use cases
For strategic implementation and maximizing value, here’s a look at how these distinct AI capabilities translate into tangible benefits across your enterprise:
Examples of agentic A in the enterprise:
- Detecting and resolving infrastructure incidents autonomously
- Managing complex service workflows from request to resolution
- Analyzing unstructured data in real time for complex problem-solving without prompting
- Optimizing dynamic processes like those in supply chains.Executing end-to-end software delivery based on evolving requirements
Generative AI use cases for businesses:
- Writing documentation or generating user stories
- Creating copy, ebooks, images, music, and videos
- Translating policies or procedures into multiple languages
- Suggesting lines of code or test cases and implementing them when asked
The examples above show a clear difference between GenAI and agentic AI: some AI tools need prompts to complete the steps in a task, while others drive coordinated action across your business.
As enterprise gets more complex, being able to design AI that not only responds but also executes across all your workflows, channels, and tools will give you a competitive edge.
How does agentic AI go a step further from GenAI?
In short, agentic AI builds on the strengths of generative AI, adding planning, reasoning, and decision-making. Instead of only responding to prompts, Agentic AI understands next steps and takes action.
Here are five key differences between generative AI and agentic AI:
- Agentic AI acts on goals; generative AI reacts to prompts.
- Agentic AI can use external tools and APIs; generative AI typically can’t.
- Agentic AI uses statistical analysis to reason through tasks; generative AI predicts the next word or token.
- Agentic AI adapts based on results; generative AI follows learned patterns.
- Agentic AI autonomously executes multistep processes across different digital platforms; generative AI needs instructions through prompts.
See how OutSystems uses GenAI to enhance business operations
Why Agentic AI
Agentic AI enables smarter, faster decisions and unlocks new levels of automation without sacrificing control and governance. It brings automation closer to human decision making, handling complexity, adapting to new information, and improving over time.
Here’s an example of how agentic AI is changing the way organizations operate:
- IT operations: AI agents monitor, troubleshoot, and resolve issues across environments.
- Customer experience: Virtual agents go beyond answering questions to completing service tasks.
- Business processes: End-to-end automation adapts to exceptions and process changes.
- Software development: AI systems plan, write, and test code based on business goals.
The result is a new level of operational intelligence that delivers business value:
- Scale automation without scaling complexity.
- Reduce human bottlenecks in decision-heavy processes.
- Increase responsiveness across teams and systems.
- Build intelligence into every layer of your digital operations.
Want to unlock agentic AI value? Develop, orchestrate, and monitor agents with OutSystems Agent Workbench.
Common questions about agentic AI and traditional AI
Causal AI uses modeling cause and effect to explain why things happen, while agentic AI focuses on achieving goals through decision-making and action in dynamic contexts.
Agentic AI works by planning, deciding, and acting toward a goal, often using tools, memory, and feedback to adapt and improve over time.
No. Generative AI creates content like text or images. Agentic AI uses machine learning, trained models, and rapid statistical analysis to make decisions and execute tasks on its own.
Rapid process automation follows predefined rules. Agentic automation adapts to real-time context, manages exceptions, and makes decisions to complete tasks autonomously.
Conversational AI powers chat and voice interfaces. Agentic AI can use conversation as one input, but it is also designed to take action, manage workflows, and achieve outcomes.
No. ChatGPT is generative AI.
Narrow AI (also known as weak AI) refers to AI systems that are designed and trained to perform a single specific task or a limited set of closely related tasks, such as recommending products or detecting fraud.