Types of AI Agents for Contact Centers

Intro

Automation driven by AI Agents allows contact centers to improve productivity, increase efficiency, cut waiting times, and ultimately, reduce costs. AI Agents act similarly to human agents, able to engage in conversations, identify user needs, carry out tasks, and resolve problems. 

As AI technology has evolved, different approaches to automation have become available. Where earlier AI models focused on Conversational AI and rule-based systems, the development of Agentic AI has unlocked a new level of autonomous, goal-oriented problem-solving. 

As such, there are multiple ‘types’ of AI Virtual Agent that function in different ways – even if they share a similar goal. As a potential adopter of AI, you need to understand what these types of AI Agents mean for your organization so you can recognize which is best for you. 

In this guide, we’ll look at how these types of AI Agents play essential roles in your automation journey. We’ll also demonstrate the value of adopting a composite approach that blends different ‘types’ of AI together to offer far more utility and cost-efficiency for your enterprise.

What is an AI Contact Center?

AI Agents are already commonplace in enterprise businesses worldwide and are particularly valuable in customer service. They are an evolution of chatbot technology that allows for more flexibility and utility. To learn more, see our article on chatbots vs. AI Agents. 

In an AI contact center, AI Agents are integrated with backend systems and perform various roles, ranging from automating background processes to directing customer interactions.

AI Agents use a combination of AI technologies to understand a user’s query and needs, produce context-relevant responses, and pursue goals to complete tasks. By interacting with backend systems such as your CRM, they can take action and resolve customer requests by updating details, identifying order status, and even rebooking canceled trips/flights. 

AI Agents are a powerful digital workforce that allows an enterprise to scale up operations within a contact center by answering more calls, reducing wait times, speeding up effective resolutions, and improving customer satisfaction without being limited by traditional labor challenges.

AI Agents are not tied to a specific channel and can be deployed across voice and text channels that include traditional phone services, but they can extend to social media and messaging such as WhatsApp. They are available 24/7 and can support multiple languages – meaning customers can reach your business in the way that suits them best using their preferred channel, speaking their native language and at a convenient time. 

Types of AI Agents

When discussing the types of AI Agents available on the market, it’s important to remember that it’s all about application and use cases. For your contact center automation journey, these use cases may include:

• ID&V AI Agent: This AI Agent is tasked with answering customer calls and collecting their details to identify or verify them.
• WISMO AI Agent: “Where is my order?” is a common source of customer service queries for ecommerce brands. An AI Agent can engage directly with customers, find their orders, and update them on their status. It can even take additional actions, such as expediting delivery or processing a return
• FAQ Bot: These bots help reduce the manual burden of dealing with commonly asked questions. A Generative AI Agent can use your internal knowledge base to produce relevant, contextual answers to a customer’s query.  
• Outbound Agent: In previous iterations of AI technology, dealing with the complexities of outbound calling was difficult. Customers may not respond as expected, and Conversational AI could struggle to adapt. Now, with Agentic AI, autonomous agents can better engage proactively with customers and adapt on the fly. 

The technical differences between types of AI don’t matter to your end customer – they just want to receive the best experience possible. However, when considering an automation journey, it’s still building a basic understanding of different ‘types’ of AI Agent. 

Always bear in mind that these ‘types’ are largely invisible to the user. They do not care about the how behind your AI Agent – they just care about what it helps them achieve. We’ll quickly cover the technical ‘types’ of AI discussed online and then explore the most important ‘types’ from Cognigy’s perspective as an AI provider for enterprise organizations… 

Technical ‘Types’ 

Across the internet, however, other publications and websites reference the following ‘types’ of AI Agents. We will cover them briefly below and then explain why, in Cognigy’s eyes, you need to consider only two ‘types’ for your enterprise.

• Simple reflex agents: This is an agent that relies on keyword recognition and follows specific, predefined rules. They have access only to immediate data rather than any contextual memory. They are used for simple tasks like resetting passwords or updating customer details in your CRM system. 
• Model-based reflex agents: This type of AI Agent is a more intelligent version of a reflex agent, using more advanced decision-making to evaluate the outcome and decide what to do next. 
• Goal-based agents: This term refers to AI Agents that can reason their way through tasks, comparing different ideas and approaches to decide what to do next. They always aim to choose the most efficient option to reach their goal. 
• Utility-based agents: Harnessing a complex reasoning algorithm to pursue the best possible outcome for a specific utility (such as a flight booking system). Unlike goal-based agents, they do not have a specific goal but aim to maximize their given utility function.
• Learning agents: As the name implies, learning agents take previous experiences into account and learn from them. The AI Agent can evolve over time by utilizing large-scale data such as call logs and its own short—and long-term memory.
• Hierarchical agents: A group of AI Agents split into tiers, with the higher-level agents deconstructing complex tasks and sending them to lower-tier agents to carry them out. 

Practical ‘Types’ 

Let’s be clear about the reality of AI Agent types. From an end-user’s perspective, the technical background differences that separate each ‘type’ we’ve covered in the list above are largely irrelevant. 

For customer service applications, the ‘types’ of AI Agents are split into two distinct categories: process-driven agents and goal-driven agents. 

• NLU-powered Conversational AI Agents use Natural Language Understanding (NLU) and are process-driven, allowing for tight control over any given interaction. 
• Agentic AI Agents use Large Language Models (LLMS) and can independently plot goals to resolve queries. 

However, in your organization, we recommend blending both of these ‘types’ together in a composite approach to achieve success. Here’s a deeper dive into each concept to show why…

NLU-Driven Conversational AI Agents

The original ‘type’ of customer-facing AI Agent was an evolution of the chatbot that leveraged Conversational AI to understand customer intent. The agent uses Natural Language Understanding (NLU) to grasp user requests and then match them to predefined dialogue flows. 

These AI Agents are highly controllable and process-driven. They are ideal for structured use cases where you don’t want the AI to improvise or go ‘off-script’, such as when processing insurance claims. At the same time, that requires anticipating and carefully scripting all the possibilities beforehand.

Agentic AI Agents

As an evolution of earlier Conversational AI models, Agentic AI Agents leverage Large Language Models to allow for contextual understanding and progressive reasoning. Rather than relying on predetermined flows, Agentic AI recognizes a user’s intent and any additional context and then makes autonomous decisions around how it can resolve the task or pass it to another agent. 

This makes Agentic AI Agents far more capable of dealing with unpredictable conversations and unusual user requests. With dynamic reasoning, the agent can be flexible and adapt to new input in a way that process-driven AI Agents cannot. 

“Composite” AI

Each ‘type’ of AI has clear value for specific purposes within a contact center. In any case that requires tight control over processes or flows, the NLU-driven Conversational AI Agent will give you the precision you need to guide customers through every step of a conversation. When customer queries stray outside of expected parameters, Agentic AI enables the agent to adapt, take action, and hold a more dynamic conversation.

With Cognigy, you can combine both ‘types’ within a workforce of AI Agents—all organized within our platform. When a customer calls your contact center, a process-driven AI Agent can answer and begin extracting identification/verification before progressing to another predefined flow or passing to an Agentic AI Agent. The customer doesn’t notice any change or difference—it all happens seamlessly within the flow of your autonomous AI Agents.

Cognigy AI Agent Platform for Contact Centers

For customers, the ‘type’ of AI Agent is irrelevant to their needs. Instead, they care about how well the AI Agent can interpret their questions and solve their problems. With Cognigy’s composite AI Agents, you can build a robust AI workforce that is capable of following tightly controlled processes where needed but also adapting to unexpected challenges or variations common in customer conversations. 

Rather than focusing on the technical minutiae behind AI technology, it’s better to grasp the overall use cases and implementations within your contact center. See more AI Agent examples here, or book a demo to see Cognigy.AI in action.