Intro

Few technologies have been as disruptive as Artificial Intelligence (AI), which has already driven widespread transformation in how enterprise businesses operate. Change has come at such a rapid scale that many organizations are still confused about the purpose of AI, especially considering all of the different buzzwords and types of AI that are discussed online. 

Two of the most important AI systems for enterprise businesses to be aware of are Generative AI and Agentic AI – with the former being popularized early by OpenAI’s ChatGPT and the latter a relatively new development that incorporates Generative AI that is set to revolutionize customer-facing AI interaction. 

In this guide, we’ll explore the differences between Generative AI and Agentic AI to help decision-makers better understand each type, explore their key differences, and share how both interact within a wider AI ecosystem that can transform your organization into a more productive, efficient, and profitable business.

What Is Agentic AI?

Agentic AI is a type of AI used to design autonomous AI Agents with ‘agency’ – meaning they can make independent decisions based on a user’s input and dynamically reason their way through a task.

That doesn’t mean they are free to do anything, but rather they are not built on flow-chart-like processes. Classic Conversational AI is like a train, it can take you from A to B on the rails that exist, but it can’t drive you door to door. Agentic AI is more like a car, it isn’t limited to just a specific set of paths but can adapt, change, and try different routes. 

Agentic AI doesn’t just process input and deliver output – it works to understand the context behind a user’s needs. It adjusts its behavior to pursue the most efficient solution to the problem. It is not a standalone technology; instead, it functions as part of a broader AI ecosystem, leveraging technologies like LLMs, Generative AI, and Conversational AI. 

In most cases, Agentic AI is synonymous with AI Agents – a term that describes customer-facing AI Agents capable of interacting directly with customers via voice or text channels. 

In short, Agentic AI is a type of AI that powers autonomous AI Agents who can interact with customers, understand their complex needs, and then break down and action all tasks needed to reach a resolution. 

Agentic AI Key Features:

1. Reasons dynamically to solve problems: Agentic AI can evaluate a user’s needs from their inputs and use contextual clues and understanding derived from both LLMs and internal knowledge hubs to determine the most appropriate solution. 
2. Uses memory to personalize experiences: Agentic AI has access to short- and long-term memory, allowing it to understand customer issues in the context of any historic challenges and also to introduce smarter personalization. 
3. Takes actions to resolve queries: Agentic AI is integrated with backend systems to allow it to carry out the tasks and actions required to reach its goal – meaning it can do anything from look up a customer’s CRM entry to verify their details to processing a customer’s booking request for a hospitality brand. 

What Is Generative AI?

Generative AI, as the name implies, is focused on generating content. Where Agentic AI is about understanding goals and carrying out actions, Generative AI is solely tasked with interpreting a user’s text or voice prompt and generating content in the form of text, imagery, and even video. 

Generative AI is trained using vast quantities of data, which it uses to identify patterns and structures. The AI then uses this knowledge to create new, original outputs. Generative AI can be finetuned with specific data, such as learning from an enterprise brand’s training materials to be able to generate new internal documentation.  

In summary, Generative AI is an output-focused technology that takes user input via voice, image or text and generates the most appropriate content as a response. In an enterprise business, Gen AI excels as a way to empower AI agents to create dynamic, personalized responses in a way that mimics human conversation. 

Generative AI Key Features:

1. Outputs creative content: The primary usage for Generative AI is to generate content that answers a user’s specific request – whether that be a full-length article or a simple reply to a question. 
2. Learns from patterns: Generative AI can recognize patterns and learn from them to replicate styles, tones, and formats – meaning it can mimic your brand voice or even the tone of a specific sub-division within your team. 
3. Configurable output: Businesses can configure how Generative AI works for them by specifying specific parameters or styles or choosing specific training data. 

What Is the Difference Between Agentic AI and Generative AI?

Both Agentic AI and Generative AI leverage Large Language Models, but the difference between them comes down to their intended goals and how they use LLMs to achieve them:

1. Agentic AI is focused on taking autonomous actions, which it accomplishes through dynamic reasoning. It is a proactive decision-making technology that interacts with your tech ecosystem to reach its defined objectives. 
2. Generative AI is focused on producing content, which it achieves by leveraging pattern recognition skills it has learned from existing data. It is a reactive technology that relies on user prompts. 

Though these AI types differ in their output, they are actually highly complementary.

Where Agentic AI can identify needs and map resolutions, Generative AI can produce any content needed as part of that process. Agentic AI is the more managerial/operational tool and Generative AI delivers output. 

As a basic example, a business could utilize Generative AI to produce a series of advertising messages based on precise inputs and then ask Agentic AI trained on internal marketing data to strategize (and carry out) a campaign that shares the messages to maximize engagement. 

Combining Agentic AI & Generative AI For Contact Center Automation

The best examples of combining Agentic AI and Generative AI come when considering AI Agents. For customer service businesses, attempting to automate customer queries can be highly beneficial for reducing costs and improving efficiency – but only if you can design an agent that is capable of navigating the complexity of customer conversations.

Agentic AI provides the dynamic reasoning needed to make an AI Agent that can freely adapt to the natural quirks and divergences a human customer may take during the conversation. Where previous iterations of AI Agents would struggle whenever a customer went ‘off script’ and didn’t follow a pre-defined, pre-mapped flow, Agentic AI can think on its feet and act accordingly – using Generative AI to produce whatever content is needed to help solve the user’s problem. 

Learn more about contact center automation in our complete guide.

Use Cases of Agentic AI in Contact Centers

As we have mentioned repeatedly in this guide, these different types of AI are most beneficial when combined within a platform like Cognigy.AI. 

This makes sharing use cases that utilize either Agentic AI or Generative AI a challenge — most use cases will involve both. However, to help you further understand the differences between each type of AI we’ve pulled together use cases that more obviously leverage one or the other, starting with Agentic AI…

Customer Support Agent

The most obvious use for Agentic AI is to build autonomous AI Agents that can act as frontline customer support agents. Before Agentic AI, AI Agents that interacted with customers needed significant work in terms of predetermined intent training, deterministic workflows, and conversation flow predictions. 

With Agentic AI, this process becomes far more fluid – instead of building a complex repository of training data with hundreds, if not thousands of intents and the required execution logic, all you need is to specify the goal, persona, and job description of the agent with language prompts. When a customer gets in contact to make their request, an AI Voice Agent will instantly answer the call, understand the query's goal, and then map any actions needed to reach it. 

For example, if a customer wanted to take out travel insurance for an upcoming trip to the USA, the agent would first authenticate them by sending a secure code, then share a personalized offer that the customer can browse before completing the transaction. During all this, the AI Agent communicates with the customer to answer queries and progress towards resolution. 

This opens up a whole new level of autonomy for customer support roles, allowing enterprises to introduce dedicated AI Agents that can accomplish complex tasks without the need for (or cost of) human involvement.  

Outbound Calling

Unlike inbound customer support, where businesses can usually predict the common support needs of customers, outbound calling is far more challenging. 

For outbound cases, it’s almost impossible to predict how a customer will receive a call or what they will say. This makes it the ideal place to implement Agentic AI – where progressive reasoning helps create more dynamic agents that can ‘roll with the punches’ and make decisions based on the highly unpredictable responses of call recipients. After all, human communication rarely follows a linear path.

Order Tracking

We’ve covered general customer support usage above, but let’s look at a specific role, too. Order tracking is a common issue for many contact centers and Agentic AI empowers far more effective AI Agents that can not only fetch order details but also take meaningful action designed to resolve the case. In our Agentic AI launch webinar, we shared an example of this exact use case – where a frustrated customer asked an AI Agent for an update on a delayed order. 

The AI Agent apologized for the delay and asked for order details, which then failed to return any satisfactory result in the backend system. Rather than simply reporting a failure to the customer, Agentic AI placed them on hold and took action – contacting the warehouse in Hamburg, Germany to ensure a clear resolution and new delivery date. 

The scenario above is a true demonstration of the power of Agentic AI. Rather than having to involve a human when it encountered an error, the AI Agent was able to recognize the customer’s frustration, offer reassurance, and then take additional action to provide a quick resolution – all within the same interaction. 

Use Cases of Generative AI in Contact Centers

Generative AI is becoming increasingly sophisticated as technology evolves – but its use cases are far more defined due to its focus on creating specific outputs. In a contact center, the capabilities of Gen AI mainly serve to cut down on labor-intensive, production-focused tasks such as:

Knowledge Hubs & FAQs

Generative AI, once trained using internal policy documents and any other relevant internal information, can be used to create detailed FAQ resources and knowledge base guides that will help answer common queries and reduce call volumes. 

Enterprise contact centers can take this concept far further with the addition of technologies like Agentic AI and Conversational AI, creating a fully interactive FAQ ‘bot’ that customers can interact with over voice or text channels to get answers to common issues. 

Call Summaries

One of the most tiresome but necessary tasks within any contact center occurs in the wrap-up stage, where human agents need to spend valuable time summarizing the call. Generative AI can automate this stage by creating instant call summaries that a human agent can quickly review and approve, saving potentially thousands of hours across a calendar year. This type of agent assistance is just one feature of Cognigy.AI’s bespoke Agent Copilot solution. 

Live Translation & Multilingual Support

Generative AI can work in hundreds of languages to generate content and translate in real-time, allowing enterprise businesses to field calls from customers across the globe and provide responses in a customer’s native tongue. 

Why Invest in Agentic AI or Generative AI?

Enterprise organizations have a clear challenge regarding customer service: how do you continue to scale into an increasingly complex customer environment? 

To maintain service quality, you need to invest in resources – but human labor is too costly to waste on many of the low-complexity, repetitive tasks crucial to successful operations.

Instead, automation via AI Agents provides a more tangible way forward. Agentic AI supports a host of integrations that enable your AI Agents to not only communicate with customers – but also to take action and resolve tasks. With Agentic AI and Generative AI, enterprise brands can finally offer human-like experiences from automated systems, which results in better customer experiences. 

When friction does occur, Agentic AI’s ability to understand customer needs and reason its way to the most satisfactory outcome means it will quickly route to a human agent – which helps minimize customer frustration and ensures your most valuable resources will only ever be used to tackle complex cases that an AI Agent can’t resolve on its own. 

Investing in AI, especially in Cognigy.AI AI Agents, will save you time, improve productivity and increase positive customer outcomes – all of which combine to result in driving cost savings across every area of your customer service process. 

The Future of Agentic AI and Generative AI

Customer service automation relies on a balance between business benefits and customer appeal. AI Agents can drive enormous cost savings, but only if your customers willingly interact with them. 

This is no easy feat, with the majority of customers expressing mistrust over AI. Many of their frustrations stem from worries about displacing jobs, providing the wrong answers or failing to secure their data.1 

Agentic AI helps organizations address this mistrust by dramatically improving customer-facing interactions. Agentic AI helps marry up other forms of AI, including Gen AI, to prioritize the most efficient path and outputs needed to serve your customers successfully. 

As with all forms of AI in the customer service industry, Agentic AI is not a replacement for human teams and is instead designed to support them and help make them more productive. 

Ultimately, Agentic AI is the next step in creating truly valuable AI Agents that not only improve things for your organization, but also provide customers with a faster, smarter, and more efficient AI Agent that is accessible across many different channels on a 24/7 basis.  

With all of these benefits in mind, it’s little surprise that a recent Gartner study2 placed Agentic AI as the most important strategic technology for 2025 and beyond – a clear sign of just how monumental this technology will be in shaping the future of autonomous agents.

Harness Agentic AI & Generative AI with a Powerful Orchestration Platform

To unlock the maximum potential of both forms of AI within an enterprise organization, you need a platform that allows you to onboard, orchestrate, and scale your AI workforce. Cognigy.AI is built to meet the needs of enterprise contact centers and allows you to train and deploy AI agents that can revolutionize your customer service processes. 

Book a demo today or visit this page to learn more about Cognigy.AI. 

External References

1:https://www.gartner.com/en/newsroom/press-releases/2024-07-09-gartner-survey-finds-64-percent-of-customers-would-prefer-that-companies-didnt-use-ai-for-customer-service 

2: https://www.gartner.com/en/documents/5850847