Artificial intelligence (AI) has been in discussion for more than a decade now. While it’s a much broader concept, the two most talked-about subfields are agentic AI and generative AI.
Two very different concepts, and yet equally powerful in their impact.
Agentic AI can make decisions on your behalf with limited supervision, while generative AI can assist you with brainstorming ideas and creating content in different formats.
It’s essential to understand the main functions of the two AI approaches in the software space and their differences so you know when, where, and how to use them.
In this blog, we will talk about agentic AI and generative AI in detail, how they work, their features, along with some practical use cases.
What is Agentic AI?
Agentic AI is a software system that works autonomously and interacts with data and tools to help you accomplish a goal. Similar to AI testing tools, it consists of AI agents that mimic real user actions, learn from external environments, adapt to changing conditions, and perform complex tasks.
A PwC survey shows that 66% of the companies using agentic AI have reported an increase in productivity, while 55% believe it helps with faster decision-making.
Agents search the web, call APIs and query databases, and act based on this information. Because these agents are driven by large language models (LLMs), you can switch tabs, dropdowns, charts, sliders, or any UI elements using plain language or voice commands.
Agentic AI features
1. Autonomy
AI agents do not need constant human input in order to do tasks or make decisions. You define a goal using text, voice, or digital channels, and they plan and decide the best way to reach the goal. They assess past interactions and available tools and systems before they execute a task.
This ability to learn and operate on their own simplifies workflows by letting machines do the complicated and repetitive tasks.
2. Problem-solving
Typically, agentic AI follows a four-step process to solve problems. Perceive, plan, act, and learn. When you input a goal, AI agents start collecting data from previous interactions, logs, or user behavior.
The LLM plays the role of an orchestrator to process this data and understand the goal. The agent then breaks the goal into smaller tasks and collaborates with tools and frameworks to execute them. It learns from feedback and gets better over time to handle complex scenarios.
3. Interactivity
Agentic AI isn’t a passive system. It engages actively with users, gathers data in real-time, and changes how it acts based on that. Think about self-driving cars. It makes accurate and safe driving decisions by accessing live traffic conditions.
How Does Agentic AI Work?
When you set a goal, an AI agent extracts data from its environment using sensors, APIs, and databases. Once the data is in place, the agent uses natural language processing (NLP), computer vision, and machine learning (ML) algorithms to interpret user queries, find patterns, and decide what actions it needs to take.
It uses decision trees to assess multiple possible actions, develop a strategy, and decide the best course of action to achieve the goal. Finally, they collect feedback and use reinforcement learning to refine strategies over time.
What is Generative AI?
Generative AI, or GenAI, is a deep learning model that can create original ideas, stories, images, videos, and music. It can be trained to learn human language, programming languages, and complex subjects such as art, science, and math.
Generative AI in software testing leverages these capabilities to design, execute, and optimize tests, reducing manual effort and accelerating release cycles.
Generative AI features
1. Content creation
Content creation is the core of gen AI. It can answer complex queries, generate images and videos, write lists, solve math equations, and even give you advice when instructed.
You can produce code using simple, natural language to streamline the software development process. This can be particularly helpful for technical teams with different skill levels.
The 2025 Key Issues Study by The Hackett Group reveals 89% of enterprises are advancing their Gen AI initiatives, compared to 16% the prior year.
2. Data synthesis
Gen AI models replicate real-world data to create synthetic datasets which can be used to train models for activities like fraud detection and demand forecasting. Synthetic data helps you create more diverse and complex datasets that enhance the quality of data used for testing apps and training machine learning models.
3. Hyperpersonalization
As gen AI models are trained using actual user inputs, they can pick up patterns and user preferences. Businesses use this information to personalize user experiences at scale and with speed. You even can create personalized emails and custom offers based on browsing history and previous purchases.
How Does Generative AI Work?
Gen AI models rely on a foundation model such as the large language models (LLMs), that enable you to produce text based on natural language prompts. There are other foundation models for image, video, and audio generation.
Developers train deep learning algorithms on large amounts of raw, unlabeled, and unstructured data to create foundation models. The models then analyze data to predict the next line in a sentence, or next element in an image, or the next line of code.
AI models leverage reinforcement learning and human feedback to enhance accuracy and relevance.
Developers constantly test model performance and use frameworks such as retrieval augmented generation (RAG) to use relevant sources outside the training data to refine parameters and fine tune responses.
Differences Between Agentic AI and Generative AI
| Parameter | Agentic AI | Generative AI |
| Purpose | Primarily execute tasks, make decisions autonomously, and continuously learns to handle complex environments | The main goal is to generate content that’s similar to human-made creations like writings, images, or videos. GenAI is often used for communication or artistic purposes |
| Core functionality | Analyzes a specific goal, breaks it down into smaller subtasks and reasons and plans autonomously to achieve the goal | Uses large datasets to learn patterns and create original content based on the said patterns |
| Type of output | Delivers functional outputs like executing tasks, developing and adjusting strategies, automating workflows, and monitoring | The output is usually static and non-actionable. Some examples are articles, product descriptions, tables, lists, diagrams, code, and sound |
| Interaction style | Autonomous. It doesn’t need constant interaction with humans. Although it collaborates with other tools and frameworks to decide the best set of actions to achieve a goal. | Collaborative. Interacts with humans through text, voice, or images to generate content |
| Technologies used | Reinforcement learning, robotics frameworks, decision trees, and sensor fusion | Transformer models, generative adversarial networks (GANs), and diffusion models |
Agentic AI vs Generative AI – Use cases
Agentic AI use cases
1. Customer service
Businesses today want high retention and increased customer loyalty. Traditional customer service chatbots are pre-programmed with a fixed set of commands and often need human support for solving complex customer queries.
Autonomous AI agents quickly understand your customer’s intent, assess the query, and take actions to resolve the issue. Unlike a traditional bot that returns a default fallback message, an AI bot thinks smart and connects the customer with a human agent in case it cannot solve an issue on its own.
2. Research and discovery
Agentic AI systems enhance your research process by formulating hypotheses, designing experiments, and analyzing results.
This can be particularly helpful in research methodologies where multiple variables need to be tested, allowing researchers to drive outcomes with scale, speed, and accuracy.
AI agents can potentially transform the discovery process, from knowledge reasoning and experimental simulation to iterative learning.
3. Workflow management
Agentic AI is capable of handling business processes independently by automating complex tasks and internal workflows.
For instance, a logistics company can automatically reorder supplies and adjust schedules and delivery routes based on real-time traffic conditions. Tasks like tracking shipments can be easily automated to optimize supply chain operations.
Generative AI use cases
1. SEO content creation
Many businesses are now using gen AI tools to create content within minutes that have the potential to rank high on search engines. You can use these tools to research articles and blogs, generate appealing images and videos, and optimize your content with keywords.
A digital marketing agency, for example, can use gen AI tools to design a keyword-optimized landing page, including graphics, CTAs, videos, and more.
2. Product development
Organizations that are into product design and development can use gen AI models to research the market, user preferences, and latest trends, and create new product concepts.
This, in turn, can speed up the product development cycle. A fashion company, for instance, can come up with new design ideas based on market analysis and consumer input.
3. Language translation and localization
Language translations have moved way beyond word-to-word interpretations. Generative AI models like ChatGPT and Gemini understand nuances, cultural context, and idiomatic expressions. Businesses that want to expand globally can do so without hiring a large team of human translators for multilingual communication.
Final Words
Agentic AI and gen AI both have unique capabilities. One isn’t better than the other. Rather, they complement each other. For example, Gen AI can generate code, and agentic AI can handle the testing and deployment.
Finally, remember, before integrating these technologies into your workflow, it’s critical to ensure they’re developed ethically and with transparency. As AI models constantly deal with user data, you must make sure the systems are secure and comply with data protection regulations.
