Agentic AI vs. generative AI: What sets them apart

When most people hear artificial intelligence (AI), they think of tools like ChatGPT or Google Gemini. This popular technology, known as generative AI, has captured widespread attention for its ability to mimic human creativity and communication. But now, a new kind of intelligence is emerging: agentic AI. As a result, more teams are asking how agentic AI vs. generative AI compare, and where each fits into their strategy.

As both technologies evolve, they’re beginning to reshape AI in the workplace. Businesses are exploring how these tools can work together to improve efficiency and customer experience across every interaction. That exploration starts with understanding how agentic AI and generative AI differ, and what that means for teams looking to stay ahead. This guide will cover exactly that.

More in this guide:

• What is agentic AI?
• What is generative AI?
• What are the differences between agentic AI and generative AI?
• How agentic AI and generative AI work together
• The future of agentic and generative AI
• Frequently asked questions
• Modernize your workflow with Zendesk AI

What is agentic AI?

Agentic AI is software that can independently make decisions and carry out tasks to achieve a specific objective. Rather than following rigid, pre-programmed rules, it can map out and complete a sequence of actions on its own.

Key features of agentic AI include:

• Autonomous decision-making

• Goal-oriented task execution 

• Multi-step reasoning and planning

• Context awareness and adaptability

You may also hear the term AI agent used in this context. While related, an AI agent typically refers to a single component within a larger system, whereas agentic AI describes the full framework that guides those agents toward a common goal.

Agentic AI use cases

Agentic AI is most valuable for tasks that require multi-step coordination and continuous optimization. Here are some specific use cases:

• Customer service: Agentic AI helps support teams handle customer requests faster and more accurately. Zendesk AI copilot, for example, supports agents by autonomously resolving common issues and managing workflows.

• IT service management (ITSM): Agentic AI automates routine IT requests and flags potential issues early through continuous system oversight.
• Cybersecurity: Rather than waiting for human input, agentic AI can detect unusual activity, isolate affected systems, and respond to emerging threats immediately.

• Supply chain and logistics: Agentic AI helps optimize routes and balance inventory across locations, keeping operations running smoothly.

What is generative AI?

Generative AI refers to models that can create new content, such as text, images, and audio. It learns from vast amounts of existing data to craft outputs that sound natural and human-like.

Some features of generative AI are:

• Produces text, images, audio, or code

• Responds to prompts

• Draws from large-scale training data

• Mimics different styles or tones

• Scales quickly for creative, content-heavy tasks

Because generative AI is designed to replicate patterns in language and media, it’s highly adaptable across a range of formats and styles. This makes it a foundational technology for many content creation and communication tools.

Generative AI use cases

Generative AI excels at creating content, summarizing information, and adapting language for different contexts. Below are some of the most common workplace use cases.

• Customer support content: For instance, Zendesk offers an AI-powered knowledge base that expands and refines content, turning notes into helpful articles to improve self-service.
• Conversational intelligence: Zendesk conversational intelligence uses generative AI to analyze and summarize customer interactions, providing agents with insights and call summaries that speed up follow-up and enhance service quality.

• Marketing content: Generative AI tools like ChatGPT can assist in brainstorming headlines, email campaigns, and social media content ideas.

• Product documentation: Generative AI can create user guides and onboarding materials based on internal notes or engineering inputs.

• Training materials: It can produce custom training modules, quizzes, and simplified explanations adapted to different roles.

What are the differences between agentic AI and generative AI?

While these two types of AI often operate side by side, they serve distinct roles. The following section breaks down the main differences in how each one works.

Role in CX

AI in customer experience (CX) takes on many roles. Generative AI supports human agents by drafting personalized replies, summarizing customer conversations, and refining help center content. It also powers conversational bots that use existing knowledge to deliver natural, on-brand responses.

In contrast, agentic AI is used to automate entire support processes—recognizing the nature of a request, gathering relevant account or order data, and resolving the issue without human involvement. By executing multi-step tasks independently, agentic AI improves resolution speed and frees agents to focus on more complex issues.

Level of autonomy 

What sets agentic AI apart from generative AI is its ability to act without constant direction. Generative AI is prompt-driven, meaning it responds to inputs to produce an output. While it’s highly flexible and useful in communication-heavy environments, it relies entirely on external direction to operate.

Agentic AI, on the other hand, is capable of taking initiative once given a goal. Rather than waiting for a prompt, it can plan and execute multiple steps, make decisions based on context, and adjust its actions as it works toward a resolution.

Underlying technologies

Both agentic and generative AI rely on machine learning (ML) to learn from data and recognize patterns. They differ in how they use large language models (LLMs) within their systems.

Generative AI is built almost entirely around LLMs, which are trained to understand prompts and produce human-like text. These models rely on natural language processing (NLP) techniques to interpret input and deliver natural-sounding responses.

Agentic AI, by contrast, uses LLMs as one component within a larger system. While the LLM may interpret a request or generate part of a response, the system also includes planning algorithms and memory modules alongside integrations with external tools. Together, these technologies enable agentic AI to make decisions, maintain context, and carry out complex tasks autonomously.

Complexity and coordination

Think of generative AI as a skilled writer crafting responses to individual prompts, producing one focused output at a time. Each interaction is independent, without coordinating multiple steps or maintaining long-term context. This is especially helpful for tasks like summarizing text or drafting emails. 

If generative AI is a writer, then agentic AI is the project manager. This is because it can handle far more complex tasks that require coordination across multiple steps. For example, it can help oversee an entire customer support case, guiding it from the initial issue to final resolution. 

How agentic AI and generative AI work together

Generative AI is transforming customer service by enabling personalized interactions at scale. By adding agentic AI, businesses can combine the strengths of both models to automate more involved processes from start to finish.

Agentic AI systems often use generative AI as a tool within broader workflows. For instance, when resolving a complex issue, agentic AI might initiate a refund, update internal systems, and then trigger a generative model to draft a follow-up message to the customer. This dynamic coordination allows businesses to maintain a high level of personalization, even as they automate the behind-the-scenes work needed to resolve issues quickly and consistently.

Together, these AI models speed up resolution times and boost agent intelligence, allowing teams to focus on higher-level tasks.

The future of agentic and generative AI

Looking ahead, AI in the workplace will increasingly rely on agentic and generative AI working in tandem to boost productivity and employee satisfaction. As these technologies take on more routine communication and tasks, human agents can focus on higher-value interactions. 

Key trends shaping their future in CX include:

• Growing focus on agentic AI: While generative AI is important, more organizations will start prioritizing agentic AI to achieve greater operational impact.
• Unified AI-powered workflows: Generative and agentic AI will be embedded into shared workflows, enabling smoother automation across the customer journey.
• Improved accuracy and relevance: Because agentic AI systems learn and adapt from interactions, future solutions will become increasingly precise in refining actions to meet customer needs.
• More personalized experiences: Advances in generative AI will enable more context-aware, adaptive content creation. At the same time, agentic AI will coordinate real-time workflows to deliver tailored and proactive customer support at scale.
• Expansion into proactive use cases: As agentic AI advances, it will orchestrate more sophisticated workflows that integrate generative AI’s predictive insights, enabling timely proactive outreach and issue prevention before customers even raise concerns.
• Enhanced agent empowerment: Next-generation generative AI will provide agents with smarter, more contextually relevant response suggestions, while agentic AI automates an expanding range of routine tasks—freeing agents to focus on complex and sensitive interactions.

As AI investment surges, the challenge for organizations is no longer whether to adopt agentic and generative AI but how to deploy them strategically. Ultimately, success depends on selecting AI solutions built to handle the unique complexities of your CX.

Frequently asked questions

Customer story

Catapult Sports

Catapult offers elite CX performance with Zendesk AI

“Zendesk AI Copilot has provided us with tools that save agents time, make their job easier, and empower them to make really good decisions day to day. Because of that, I have a team that really enjoys coming to work.”

Thomas Powers

Vice President of Product Support

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Modernize your workflow with Zendesk AI

AI in customer service has become a non-negotiable for businesses aiming to meet customer needs and deliver top-notch experiences. With agentic and generative AI rapidly advancing, success will depend on carefully selecting solutions that can seamlessly integrate advanced AI into complex customer service workflows.

By leveraging the strengths of both agentic and generative AI, Zendesk AI enables instant, personalized resolutions that boost agent productivity and customer satisfaction. Built specifically for CX, it understands the unique needs of service interactions and delivers context-aware support, empowering your team to focus on what matters most.