Did you know that different artificial intelligence (AI) automation solutions not only generate content and help you brainstorm ideas but also answer your customers’ questions, help them manage their accounts, process refunds, and even upsell your products? AI has become one of the most talked-about technologies today—but not all AI systems are built to do the same thing. Two of the most recent types of AI technologies are generative AI and agentic AI; however, not many people know the difference.
So, agentic AI vs generative AI—what’s the difference, or are they the same thing?
That’s what we’ll explore in this blog post.
Keep reading to find out:
- Agentic AI vs generative AI—what’s the difference?
- Is agentic AI the same as ChatGPT?
- Which of these technologies is best for customer support and improving customer experience?
What is agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously pursue goals by making decisions, planning actions, and interacting with the world or digital tools. Unlike traditional AI, which waits for direct instructions and performs predefined tasks, agentic AI can:
- Break down complex objectives
- Adapt to unexpected outcomes
- Continue working toward results with minimal human input
With the agentic AI market projected to reach USD 50.31 billion by 2030, these systems are becoming an irreplaceable part of our personal and professional lives.
- They can plan and execute tasks
- They can make decisions in dynamic environments
- They can take actions
- They can learn and adapt to their results
The key is the word “agentic,” which means that the system can take agency in acting without being prompted every time. A good example is intelligent virtual AI agents that can offer upselling opportunities when appropriate or guide customers when an issue occurs.
But it has more use cases than that. Let’s take a look at some of the key agentic AI features.
Independent decision-making
A survey on agentic AI capabilities found that 62% of companies investing in agentic AI expect to more than double their investment, with an average projected ROI of 171%.
The numbers are backed by agentic AI’s capability to analyze situations and choose the best path forward without needing precise instructions from humans. It evaluates options, weighs risks, and selects actions tailored to its goals. For example, when interacting with customers, an AI agent can route a customer to the right department or suggest a replacement for a product.
Task execution
Instead of performing just one step, agentic AI can carry out entire tasks from start to finish. It might plan, schedule, and send follow-up emails for a project on its own or automatically manage inventory by ordering supplies when stock runs low. A great example of independent task execution is an agentic AI chatbot that helps customers complete tasks like booking a hotel room without human agent involvement.
Multi-step reasoning
These systems can break complex problems into smaller parts, solve each piece, and combine the results logically. That makes them capable of handling workflows like diagnosing technical issues, researching solutions, comparing outcomes, and then applying the fix.
For example, a customer contacts your AI-powered support to return an order because the piece of clothing they ordered was not the color they expected. Based on their response, the AI can process a refund and suggest other models.
Adaptability
Agentic AI learns from what happens after it acts, allowing it to adjust its behavior based on feedback or changing environments. If a strategy stops working—for instance, when an interaction with a customer doesn’t go as planned—it can revise its approach or escalate the issue to a human agent.
What is generative AI?
Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, code, or video, based on patterns it has learned from existing data. Instead of just analyzing or classifying information, generative AI produces original outputs that resemble human-created work.
You’ve probably seen:
- AI chatbots that write conversational responses
- Tools that generate realistic artwork
- Systems that compose music or design products
With 800 million weekly active users, ChatGPT is one of the most popular generative AI platforms and examples of this technology. But ChatGPT is only one of many new tools entering the market. In fact, the generative AI market is projected to reach USD 400 billion by 2031.
In a nutshell, it works by learning patterns from large amounts of data and then using that knowledge to predict and create new content. It doesn’t copy specific examples—instead, it generates fresh outputs that follow the learned style, structure, or rules of the data it was trained on. However, it generally doesn’t have agency and needs a person or system to prompt it. Let’s go over some of the main generative AI features and use cases.
Content creation
Generative AI is best known for its ability to produce text, images, music, and videos, helping with tasks like writing articles, designing graphics, or generating marketing material. It takes only a prompt from a user to generate a completely new piece of content. For example, you might prompt generative AI to create a polite email to a coworker.
Personalization
Generative AI systems can tailor content or recommendations to individual users, such as customized product suggestions or adaptive learning materials. For example, generative AI in customer service can detect a sentiment and adapt its tone to match the tone of a customer. Say, a customer gets frustrated, then generative AI changes its tone to defuse the situation.
Creative assistance
Generative AI tools are well known for their ability to support the creative process. If you even started at the blank page, AI can help you get started, come up with ideas, first drafts, prototyping, editing, and exploring other creative ideas and concepts.
So what are the differences between agentic AI vs. generative AI?
When it comes to generative AI vs. agentic AI, their main difference lies in autonomy. Agentic AI can act independently, make decisions, and pursue goals with minimal human input, while generative AI requires prompts or instructions from a person or system to produce output. Many people assume that platforms like ChatGPT or Claude are agentic AI. However, most of their current capabilities fall under generative AI.
To get a better idea, let’s compare the two side by side.
Agentic AI vs generative AI: comparison chart
| Criteria | Generative AI | Agentic AI |
| Main purpose | Create new content like text, images, etc., based on learned patterns | Achieve goals by planning, making decisions, and taking actions. It can be used for forecasting, using predictive AI features |
| Autonomy | Low to moderate—requires human prompts or oversight | High—can operate independently with minimal guidance |
| Scope (flexibility) | Focused on specific content-generation tasks | Broader capabilities across multiple steps and environments |
| Learning and improvement | Typically improves during training; limited self-improvement after deployment | Machine learning, behind the scenes, allows it to learn from actions and adapt in real time |
Pros and cons of agentic AI
Agentic AI offers many benefits for personal and professional use, such as greater autonomy, saved time, and flexibility. But it might not be right for what you’re looking for. Explore some of the pros and cons of agentic AI to see if you could benefit from using AI-powered agentic tools in your business.
Pros:
- Greater autonomy: It can complete tasks with little human intervention and even offer agent assist features for support teams
- Efficiency and productivity: It handles and automates complex workflows end-to-end
- Adaptability: It adjusts its actions based on feedback and changing conditions
- Scalability: It can manage large or repetitive tasks consistently
Cons:
- Safety and control challenges: While you can set guardrails, autonomous decisions may lead to unintended outcomes
- Complex implementation: It requires advanced planning, monitoring, and system design to function independently
- Higher computational and resource costs: It’s more demanding than traditional AI
- Ethical and accountability concerns: It’s unclear who is responsible if or when mistakes occur
Pros and cons of generative AI
Just like agentic AI, generative AI has its own advantages and disadvantages. To make the right decision about whether this technology is right for you, explore some of its main pros and cons.
Pros:
- Boosts creativity and content production: You can brainstorm ideas and generate text, images, code, and more at high speed
- Improves personalization: It tailors outputs to individual users or needs
- Enhances innovation: It helps brainstorm ideas and design new products or solutions
- Supports other AI systems: It creates synthetic data for better training and testing
Cons:
- Risk of inaccuracies or misinformation: Generative AI can hallucinate and generate content that’s factually wrong or misleading
- Concerns over copyright and originality: Because AI learns from existing content, its outputs might resemble existing work too closely
- Bias reinforcement: Depending on the data generative AI is trained on, it can reflect or amplify biases
- Potential misuse: It can be used to create harmful or deceptive content, such as deepfakes
Agentic AI vs generative AI: What’s best for CX?
While agentic AI and generative AI can both be used for personal tasks, their true potential shines when you combine these technologies for customer support tasks such as:
- Answering FAQs
- Helping customers with tasks like refunds, bookings, and cancellations
- Upselling
- Managing accounts
Each use case is different and depends greatly on your industry. Let’s walk through a few agentic AI vs. generative AI examples and explore how companies can implement and benefit from these technologies.
Healthcare
Companies in healthcare have been successfully using agentic and generative AI for quite some time.
For example, using generative AI, you can create:
- Medical report drafts
- Patient education materials
- Clinical summaries
To support your research, website, or any other content, you can generate synthetic medical images or data to help train diagnostic models.
More advanced tools can also help healthcare companies personalize testing and improve diagnostic accuracy. That’s what Tempus AI Inc., a healthtech company, is trying to achieve by developing Tempus TEM—a personalized laboratory testing technology based on generative AI to improve the patient experience and speed up the whole process.
Agentic AI, on the other hand, handles more complex cases like managing patient scheduling and follow-ups autonomously, assisting with treatment adherence by monitoring data, and triggering interventions when needed.
Retail
Retail is a great example of how companies implement generative and agentic AI to improve customer experience and provide:
- Personalized attention
- Faster service
- Relevant product recommendations at the right time
Retail companies use generative AI to produce personalized product descriptions, ads, and marketing content. This technology can also create visual mockups for product designs and store layouts. In customer-facing tasks, generative AI can generate on-brand responses, adjust replies to customer sentiment, and even help your team by creating interaction summaries or suggesting replies.
Retail agentic AI takes it up a notch by automating:
- Inventory management
Restocking - Supplier coordination
It can also dynamically adjust pricing and promotions based on demand and trends. Your customers can use agentic AI to find relevant products, process returns and refunds, or make changes to their orders without waiting for human agents.
Walmart Inc. is experimenting with agentic AI tools in its stores to improve the shopping experience. Its strategy is to focus on very specific cases and train AI agents on particular tasks to achieve the most accurate results. Walmart uses agentic AI to power its personal shopping agents that connect retailers, providers, and customers. On top of that, it’s already successfully using customer support assistants that handle routine and repetitive customer inquiries.
Financial services
Although financial services are often trickier to automate due to high regulation and compliance requirements, many companies in the industry successfully use generative AI and agentic AI.
Fintechs, banks, credit unions, and other financial institutions use generative AI-powered chatbots and voice bots to:
- Create and update FAQs
- Ensure regulatory compliance in communications
- Analyze feedback to predict issues before they escalate
Agentic AI for the financial industry offers more advanced customer service options, such as executing automated trading strategies with real-time decision-making or detecting fraud and initiating responses like account alerts or temporary holds. Agentic virtual assistants answer routine queries, while co-pilot tools help human agents draft accurate, empathetic responses and summarize customer interactions.
AI-powered customer support agents can handle over 90% of customer inquiries—at least, that’s been the experience of The Mortgage Collaborative, a nationwide professional network of mortgage bankers, banks, credit unions, and mortgage service providers.
They struggled with connections between lenders and providers, as most of the work was done manually. To solve this, they partnered with Capacity, an internal and external support automation solution. Together, they built a “Frankie” concierge that assists web visitors. It proactively engages users and helps deflect the majority of inquiries, saving the company over 160 hours per year.
Customer support
Customer support centers offer one of the best platforms for deploying generative and agentic AI. As 87% of customers support companies report increasing customer expectations, AI technologies offer a solution.
Generative AI can create high-quality responses for chatbots, emails, and knowledge bases. It can also translate and rewrite content to support multiple languages.
Generative AI can further predict the nature of customer calls to deflect and personalize them. That’s what Verizon Communications Inc. is doing by implementing generative AI to better predict why customer calls are happening and match them with the right agent, thereby improving service and reducing churn.
Agentic AI, on the other hand, handles full support workflows:
- Ticket routing
- Status updates
- Follow-up actions
- Agent assist tasks
However, there are plenty of other customer support examples and use cases, depending on the industry. If you’re curious about your industry, we invite you to check out use cases for generative AI in the insurance industry and ways banking can use AI to improve customer support.
Generative vs agentic AI— meet the tool that combines both
Faster customer support, personalized attention, more upselling opportunities, saved time, and reduced costs are just the beginning of the benefits you gain from implementing generative AI, agentic AI, or both. The best way to get the most out of AI technology is to combine its features and power.
That’s where Capacity, an AI-powered customer and employee support solution, comes in. It offers features like conversational AI, full AI workflow automation, AI agent assist tools, over 250 integrations, and more. If that sounds like something your business could benefit from, don’t wait—book a demo today!
FAQs
Generative AI creates new content, like text, images, or code, while agentic AI autonomously takes actions, makes decisions, and completes goals.
They evaluate available information, predict possible outcomes, choose the best action toward a goal, and then monitor results. To manage risk, they use guardrails such as predefined rules, human-in-the-loop checkpoints, and continuous feedback to correct or halt unsafe behavior.
Use generative AI when you need content creation, summarization, personalization, or ideation. Use agentic AI when you want automation of multi-step workflows, independent decision-making, or systems that take actions on behalf of the organization.
Often, the most powerful solutions combine both—generative AI for reasoning and language, agentic AI for execution.
Key concerns include loss of control, accountability when autonomous decisions cause harm, unintended actions, data misuse, bias in decision-making, and ensuring humans remain able to oversee and intervene.
In short:
Machine Learning (ML) is the broad field of algorithms that learn from data to make predictions or decisions without being explicitly programmed.
Generative AI is a subset of ML focused on creating new content.
Agentic AI is what builds on generative models but adds autonomy—the ability to set goals, take actions, use tools, and interact with environments to achieve outcomes.
These three technologies offer unique use cases, like:
Predictive AI uses data to forecast future events or outcomes
Generative AI produces new data or content (e.g., text, audio, video)
Agentic AI uses predictive and generative capabilities to make decisions, plan steps, and perform tasks autonomously in pursuit of goals