Agentic AI vs. Generative AI: What’s the Difference & Why it Matters

Table of Contents

1. What Is Agentic AI?

2. How Agentic AI Differs from Generative AI

3. Why Agentic AI is Critical for Customer Experience

4. Key Benefits of Agentic AI in Customer Service

5. How Agentic AI Works: From Decision-Making to Automation

6. Real-World Use Cases of Agentic AI

7. Challenges and Risks of Agentic AI

8. The Future of Agentic AI in Customer Support

9. FAQs

In 2025, Tony Stark’s Jarvis still seems to reside in the distant future— but Agentic AI is a promising technology that might as well bring the world closer to a Jarvis-like, nearly-sentient AI.

Agentic AI is thriving today, and it’s automating tasks, improving decisions, and making things run smoother than ever.

How? With AI-powered chatbots, virtual assistants, and agent productivity tools that handle routine tasks and improve customer experiences. A recent McKinsey survey proves it by stating that 45% of companies using AI have cut costs and increased revenue. When it comes to agentic AI, this means going beyond automation—it’s about AI systems that can make autonomous decisions, adapt to new situations, and even set their own objectives.

In this blog, we’ll break down what Agentic AI is, how it’s different, and why it might be the key to taking customer service to the next level.

What Is Agentic AI?

A next-generation development of generative AI, agentic AI is intended to function with increased intelligence, flexibility, and autonomy. That makes agentic AI an essential productivity-catalyzer that the modern-day organizations are slowly starting to discover and assimilate.

According to recent Gartner research, less than 1% of enterprise software applications utilized agentic AI in 2024. This figure is projected to rise significantly to 33% by 2028, reflecting the growing recognition of its value.

Agentic AI is capable of autonomous decision-making, such as navigation and control in autonomously driving cars, or resource optimization in supply chain management. It can also perform proactive action and continual self-optimization in real time in contrast to Generative AI, which adheres to preset rules.

To paint a detailed picture of its surroundings, agentic AI pulls data and insights from all technologies around it, such as sensors, databases, and digital interfaces.

For specific functions like content creation or visual processing, agentic AI uses large language models to orchestrate task understanding and solution generation. 

Integration with external tools and enterprise software helps agentic AI execute tasks based on the plans that it formulates autonomously. While agentic AI can formulate plans and make decisions, it still needs external software to act on those decisions. Integrations connect apps like CRMs to agentic AI, allowing it to customize and send communications to customers autonomously.

It is crucial to integrate agentic AI with your systems in order to improve their execution capabilities, enable real-time adaptation through the use of live data, and improve decision-making through the utilization of interconnected datasets.

How Agentic AI Differs from Generative AI

Agentic AI offers a substantial improvement over classical AI in terms of capabilities. While generative AI depends on human input and pre-established rules, agentic AI is distinguished by its independence, flexibility, and goal-drivenness.

Here’s a quick breakdown of the properties unique to agentic AI:

Let’s explore these parameters in depth:

1. Autonomy

Generative AI operates with a predefined set of rules and parameters beyond which it cannot function. If the results are not accurate, human intervention is needed to readjust these rules and parameters.

However, agentic AI is capable of knowing the environment in which it operates, making decisions for itself, and aligning those decisions with its goals.

For example, an AI-enabled Chatbot operates based on a predefined set of answers and chat flows. While an agentic AI-enabled chatbot will receive user’s query, collect relevant data from different systems, and autonomously decide how to respond to his or her query.

2. Learning

Supervised learning is what most classic AI experience, whereby after much processing and labelling, these datasets are fed into the system. In contrast, agentic AI employs unsupervised and reinforcement learning, where it learns as one interacts over time.

For example, for FAQs like delivery delays, generative AI can flag the question as common but cannot refine its service level. It will reply with the same standardized response that it is programmed with. However, agentic AI can analyze whether delays are due to warehouse locations or other factors and provide more informative responses to customers.

3. Adaptability

Generative AI approaches tasks in a task-specific, set manner. On the other hand, agentic AI is more flexible and adaptable, adjusting its behavior depending on current circumstances to achieve its goals.

For example, if a customer suddenly changes their query from the refund to store policies, generative AI is likely to get “confused.” However, agentic AI can adjust to the new shift in context and provide relevant responses.

4. Orientation

Compared to agentic AI, generative AI systems are hardwired with respect to tasks or work (for example, process automation). In contrast, agentic AI systems are goal-oriented in that they generate actions autonomously to attain certain positive outcomes.

For instance, generative AI would answer, “Sorry, I don’t have the answer for that,” for any question outside its programming. In contrast, agentic AI works toward a goal and will concentrate on satisfying the customer irrespective of the type of query raised. It is adaptive enough to return the correct response.

5. Context Awareness

Generative AI does not possess continuous awareness of its surrounding context, as it relies exclusively on supervised learning and, hence, the restrictive datasets. Agentic AI becomes almost fully context-aware, as it is tied to external systems that let its learned operational environment be understood.

For example, an AI would be assigned to assist a customer with troubleshooting. A routine AI can simply give you a bunch of generic responses through static scripts without understanding the full context of the customer’s issue. 

In contrast, an agentic AI would obtain real-time information, know the resolved case (for example, identify a network outage as the cause of the problem), and recommend a personalized solution.

Why Agentic AI is Critical for Customer Experience?

Agentic AI has the potential to revolutionize customer experience because of its autonomy and capabilities to handle complex tasks.

Here’s why it’s a game-changer:

1. Personalization

According to HBR, 80% of customers want brands to provide personalized experiences. Agentic AI accesses enterprise systems to extract customer insight and provide hyper-personalized experiences.

2. Handling Complex Issues

Customer service frequently deals with intricate problems that typically require escalation from chatbots to human agents. However, with its advanced context-awareness and high degree of autonomy, agentic AI can independently resolve such challenges. This capability eliminates the need for human intervention, transforming the customer experience (CX) and setting new standards for efficiency.

3. Predictive Analytics for Proactive Service

Agentic AI focuses on goals and adapts its methods to achieve them. This makes it highly effective in predictive analytics and sentiment analysis, allowing it to detect and resolve potential customer issues before they arise, ensuring smoother interactions and better experiences.

Key Benefits of Agentic AI in Customer Service

Agentic AI can revolutionize customer experience with its vast possibilities. Especially in customer service, agentic AI’s capability for 24×7 learning and improvement, scalable personalization, decision-making autonomy, and contextual awareness lead to several benefits in the field.

1. Informed Decision-making

Agentic AI makes it possible to make smarter decisions as it can process vast amounts of customer data from different sources in real-time. They detect patterns in data faster and with better accuracy, empowering confident service decisions.

For example, agentic AI can tell customers the status of their order, why it is delayed, or what the revised timeline is.

2. Increased Productivity

Agentic AI is capable of real-time decision-making and continuous learning, helping to fully automate support and streamline workflows with better efficiencies without much supervision. This gives human agents more time to focus on critical thinking.

3. Automation of Complex Workflows

Agentic AI is goal-oriented and capable of accessing enterprise data for autonomous decisions, making it suitable for automating complex ticket workflows with multiple execution layers.

4. Adaptability and Quick Response

Agentic AI is capable of interpreting real-time interaction data and making rapid decisions to adapt its strategy to achieve specified goals. This eliminates the need for constant human intervention, empowering quick responses and favorable outcomes.

5. Augmenting Human Roles

Agentic AI can augment human tasks by handling actions such as generating quick, context-aware responses, improving communication, and offering personalized assistance. This allows employees to concentrate on more valuable, strategic activities. By fostering collaboration between humans and AI, it promotes a balanced and innovative work environment.

How Agentic AI Works: From Decision-Making to Automation

Agentic AI uses a comprehensive four-step process to solve any problem:

1. Perceive

This involves gathering and processing data from diverse sources and extracting meaningful features from them. The AI then identifies relevant entities in the environment to solve problems.

For example, agentic AI integrates with CRM systems to retrieve customer data, not just for reference but also to analyze patterns and gain a deeper understanding of customer needs.

2. Reason

The AI then accesses an LLM to understand the tasks, generate solutions, and deliver accurate and relevant outputs to the user. The model may also use Retrieval-Augmented Generation (RAG) to gain access to proprietary data sources and ensure its production is as relevant as possible.

For example, agentic AI uses the data pulled from CRM to augment its understanding of the customer’s situation or query.

3. Act

The AI model then accesses external apps or software via APIs to execute the tasks based on its planning. For example, agentic AI can process insurance claims autonomously— but you can program the upper limit of the amount it can independently process.

4. Learn

Agentic AI uses a continuous feedback loop called “data flywheel”, where the real-time data it generates is input into the system to enhance its model. This enables the agentic AI to become better with time and more interactions.

For example, agentic AI will learn from one customer interaction to apply insights to the next. This way, it can continuously refine its responses and anticipate user needs more accurately.

Real-World Use Cases of Agentic AI

Agentic AI has several key use cases in customer experience across various industries:

1. Insurance: Streamline Claims

Agentic AI is capable of making autonomous decisions, empowering it to process, approve, and disburse insurance claims all by itself. This has the potential to transform the insurance industry and greatly reduce agent workload.

2. Retail: Customer Support

The eCommerce and retail industries see quite a few complex customer support cases that require frequent escalation from chatbots to humans. Agentic AI is a context-aware solution that can access data from external apps and sources to develop a deep understanding of customer issues and provide solutions to even the most complex problems.

3. Healthcare: Patient Care and Diagnostics

The healthcare industry works with large volumes of patient data that can be difficult to access. Agentic AI can pull patient data across all touchpoints and collate it to develop a holistic patient profile for better diagnostics and treatment.

4. Finance: Risk Management and Fraud Detection

Since agentic AI can learn in real time, it is the perfect tool for modeling both historical and contemporary attack patterns on digital data.

Instead of waiting for human assistance, it can be crucial in seeing data irregularities, spotting fraudulent conduct, and acting quickly to stop it. Where generative AI typically responds to identified threats only, agentic AI predicts fraudulent patterns and prevents them.

Challenges and Risks of Agentic AI

Agentic AI is powerful and can reduce the need for human effort. However, some challenges must be addressed to ensure ethical use, data security, accuracy, and reliable decision-making.

Here are some key challenges to consider:

1. Independence and Supervision

The autonomous nature of agentic AI begs the question of how much machine autonomy is appropriate. To ensure that unforeseen events don’t damage your business’s reputation, it’s critical to balance the advantages of autonomy against the potential danger to the customer experience.

2. Transparency and Trust

Agentic AI is capable of making decisions on its own without pointing out the stages or the procedure it used to get there. This lack of transparency may cause problems with confidence and cast doubt on the accuracy and fairness of the algorithm.

3. Privacy and Security

Generative AI models can only access the data that they are provided. However, agentic systems have access to sensors, databases, enterprise apps, etc. to gather data. This can bring sensitive or protected data within the purview of the model, which is not desirable. You can emphasize strict security controls and decision governance protocols as fail safes that mitigate risks.

4. Inaccuracy and Ethical Failure

Agentic AI makes decisions independently, which emphasizes the need for ethical guardrails. It helps prevent unintended actions that harm your company’s reputation or resources based on its understanding of a situation as it acts towards the goals.

5. Loss of Control

Agentic AI systems are complex and self-evolving. Additionally, they are autonomous, which could present challenges for control. You can mobilize robust governance frameworks and override mechanisms that ensure you retain full control of your AI system.

The Future of Agentic AI in Customer Support

Agentic AI systems have a promising future in the field of customer service, particularly in areas where generative AI systems fail, such as decision autonomy and delivering relevant service to customers.

For example, customer service AI will transform from task-specific to a multi-domain asset that understands nuanced context and delivers great results.

With agentic AI, customer service is poised for a better future with:

• Better contextual understanding: Enhanced NLP capabilities will allow agentic chatbots to comprehend complex human language and its context.
• Improved collaboration: Agentic AI can integrate with external systems, empowering better collaboration in real-time with more synergy.
• Autonomy: Agentic chatbots will deliver enhanced customer service and experience with autonomous decisions informed by the vast amounts of data the customer has shared with a brand.

Final Words

With agentic AI, brands can reinvent their customer experience, balancing the contribution of humans with AI, enhancing productivity, and improving collaboration and connectivity within the office.

The future of customer experience is AI-driven, and Kapture CX offers a comprehensive suite of AI-native solutions with agentic AI capabilities to transform customer interactions, automate workflows, and drive business growth. 

Using our chatbots and virtual assistants with agentic AI, brands can deliver instant, intelligent responses, reducing resolution times. Additionally, sentiment analysis and predictive insights help businesses proactively understand customer emotions and anticipate their needs.  

Empower your CX teams with AI—connect with our team today!

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