Intro

The rapid development of Artificial Intelligence has enabled enterprise businesses to truly revolutionize customer service processes by introducing dedicated AI Agents that help engage with customers, solve problems, and save time and money.

Though existing AI Agents can simulate human-like conversation and generate relevant responses through Conversational AI and enhanced with Generative AI, they are still reliant on predefined conversation flows which can lead to errors if the AI fails to understand the customer. 

It is in this failure that customer trust is harmed, with traditional bots forcing rigid conversational experiences that often fall apart if a consumer digresses from the intended topic. 

For your business to benefit from the cost-efficiency and productivity gains associated with AI adoption, it must also address these concerns. To achieve that, you need an AI Agent that is capable of ‘thinking on its feet.’

Enter Agentic AI – a groundbreaking evolution that allows enterprises to deploy autonomous AI Agents that give customers the most human-like experience possible. 

What is Agentic AI?

To best understand what Agentic AI is and why it matters to you, you need to think of AI Agents as a type of User Interface (UI). Like a Graphical UI (GUI), where design elements help users navigate options and make decisions, an AI Agent essentially acts as an interface between your business processes and a user speaking naturally in their native language. 

Agentic AI is the next evolution in this interface, creating AI Agents capable of autonomously performing tasks by making independent decisions, with reasoning driven by Large Language Models (LLMs) that allow the agent to analyze a customer’s input, reason through their goals and process any number of required actions to reach its goal – all while following any procedures or requirements you need to protect your enterprise. 

All of this combines to mean that, unlike previous versions of AI Agents that relied on predefined conversational flows, Agentic AI Agents are capable of independent decision-making and can solve complex problems through dynamic reasoning. 

Ultimately, this technology represents a bold evolution in intuitive conversational interfaces, giving your business all of the benefits of automated chat agents while providing customers with flexible interactions that are as fluid, personalized, and solutions-focused as human-led customer service conversations. 

Why Agentic AI Matters

Nothing turns a user away from AI faster than feeling like they’re interacting with a rigid, robotic companion. Agentic AI helps create a more engaging customer service interface by employing dynamic reasoning and utilizing short and long-term memory to understand and better contextualize user requests, break them down into tasks, and then chart the most efficient path to resolution whether that be informational or transactional.

Agentic AI is a groundbreaking technology because it directly resolves some of the core issues associated with AI in a customer service environment. In a survey of customer concerns around AI in customer service, 42% of users said a major concern was that chatbots would provide the wrong answers. While this is a valid concern, the same issue among human agents is often forgotten (Agent Error Rate) or not compared due to many people ironically having higher expectations from AI. 

By training an Agentic AI Agent using your enterprise knowledge base, it can dynamically reason its way through even the most complex user queries to find accurate answers among the official and approved data you give it and provide customer support. 

Furthermore, Agentic AI agents can perform contextual handovers to human agents – addressing yet another common concern for users who worry that speaking to an AI Agent would make it difficult to reach a human representative. In fact, it will make conversations with human agents more effective and satisfying as they will start equipped with all the facts instead of having to play 20 Questions like in the past. 

Finally, Agentic AI makes it easier than ever to train new AI Agents for new tasks within your enterprise. Each agent can be trained with natural language prompts in a similar fashion to how you would train a new human agent. 

Together, these capabilities make Agentic AI one of the most significant evolutions in AI technology. 

Learn more about Agentic AI vs AI Agents.

How Does Agentic AI Work?

An AI Agent acts in a similar fashion to human agents. They listen, reason, act, and respond much the same way as humans. To achieve this level of autonomy, Agentic AI agents utilize a combination of NLU-driven Conversational AI and LLMs to act within the freedoms you give them, drawing initial learning from databases and networks and then continuing to learn ‘on the job,’ adapting to customer behavior.

Unlike a conventional bot which relies on predefined paths, Agentic AI will break requests down into actions and use whatever tools it requires to carry them out as efficiently as possible. 

The graphic above demonstrates the difference between a more linear intent-driven AI and an Agentic AI workflow, but we need to dig deeper into the anatomy of an Agentic AI agent to show how capable they are of performing this type of task breakdown. To achieve

The Anatomy of Agentic AI

To understand how Agentic AI works, you need to see the layers involved in how the AI Agent achieves this unparalleled level of autonomy. Let’s take a closer look at the layers that make up Cognigy’s bespoke solution: 

Composite AI with NLU and LLMs

The foundational layer of any Agentic AI system is an LLM, used in combination with NLU-driven Conversational AI which allows it to understand language and everyday user interactions while maintaining a high degree of flexibility but the ability to follow strict processes where required Being technology and vendor agnostic, Cognigy offers AI Agents that utilize LLMs such as OpenAI, Azure, Anthropic, or any other you may chose including your own. 

CX Finetuning 

For Cognigy’s Agentic AI Agents, an additional bespoke layer is added above the LLM, which utilizes years of CX experience to cater to the unique requirements of enterprise contact centers, providing omnichannel connectors, contact center connectivity, enterprise-grade security, and control, among others. 

Enterprise Knowledge 

Every AI Agent is different and has a specific role. Though Agentic AI agents use LLMs for general foundational knowledge, they must also be trained on data specific to your organization and sector, such as FAQs, policy documents, and product information. 

AI Agents can be trained at both the agent and job levels. Information provided at an agent level gives it access to general knowledge regardless of what task it performs – such as knowing your company’s policies to keep every interaction compliant with certain rules or expectations. At a job level, you can provide specific skills or information needed for an agent to perform a dedicated task. 

For example, an airline company can train an AI Agent with comprehensive FAQ documentation to have it act as a customer-facing service role aimed at answering popular questions. 

Skills & Actions 

For an AI Agent to be truly autonomous, it must be able to carry out tasks without having to ask humans to intervene. Agentic AI allows you to give an AI Agent specific skills related to tasks, such as creating a support ticket or looking up a customer’s CRM entry. In enterprise organizations, this often means creating a team of AI Agents trained to complete specific tasks. If the agent can’t solve a customer’s request, it can hand the case to another AI Agent who has the necessary skills. 

Progressive Cognitive Reasoning

This is, perhaps, the most important ‘layer’ for understanding how Agentic AI works. It refers to how an AI Agent can evaluate a user’s intent based on their input, read contextual clues, and consult all of its available knowledge to determine the best response or action. It breaks user needs down into logical goals and will plan the most efficient path to resolution.

Memory

The AI Agent comes equipped with memory functions that allow it to remember information within a conversation (short-term memory) and also to recall information across multiple interactions (long-term memory). This additional layer of memory helps the AI Agent personalize interactions around a user’s historic preferences and needs in addition to its integration with your CRM and accompanying data. 

Safety

A final, additional safety layer can be added to an AI Agent to ensure its behavior stays within predetermined guardrails and protects it against jailbreak attacks and other malicious activity. 

In isolation, Agentic AI agents all share the power to reason through tasks. Only when presented as part of the Cognigy.AI framework, however, can the AI Agent come equipped with immediate multilingual, omnichannel capabilities and 100s of pre-tested integrations. 

What Is the Difference Between Generative AI vs Agentic AI?

While based on the same underlying technology, which is large language models (LLM), Generative AI and Agentic AI are meant for different goals and functions.

Generative AI focuses on output – it takes a user’s input and outputs content based on the context of said input. Gen AI is sometimes used to enhance the predefined responses of a Conversational AI system by recreating context-relevant variants. Agentic AI, on the other hand, is action-orientated – it breaks down a user’s goal into a series of tasks and carries them out autonomously. 

Where Generative AI is all about creation, Agentic AI customer service is about action. In a customer service context, Agentic AI takes a customer’s input and breaks it down into actionable tasks – using Generative AI to create contextual, personalized responses as it converses with a customer and also for any additional content that may be required such as FAQ answers. 

Learn more about Agentic AI vs Generative AI.

What Are The Benefits of Agentic AI?

Agentic AI is a transformative technology that will help usher in a new generation of customer-facing automation. It carries a range of compelling benefits that apply to both your organization and to users, including:

Dynamic Reasoning Streamlines Performance

Agentic AI allows your AI Agents to reason their way through tasks in a way that was previously impossible. Rather than causing errors or presenting false information when a customer deviates from the expected parameters of a conversational flow, Agentic AI can respond dynamically and tailor every response to the specifics of a customer’s call. 

This also makes onboarding your AI Agents far easier, as you can provide initial training and then allow the agent to make independent decisions based on your knowledge base, initial instructions, and customer needs. 

Offers Multilingual, Omnichannel Service

Agentic AI Agents can communicate with users in multiple languages and can be deployed amongst many different channels, from voice calls and audio messages to text messages and digital chat agents available via social media messaging apps like WhatsApp. 

Provides Smoother Customer-Facing Experience

Customers are growing more comfortable with AI2, citing quick response times and 24/7 availability as the main reasons they may use it. They do, however, still want to speak to a human to resolve more complex tasks.

Agentic AI offers customers a way to get near-instant support for their problem with an AI Agent that communicates with them in a human-like manner. Where AI Agents once struggled with more complex needs, Agentic AI helps create a pathway to resolution. Over time, this may be enough to show customers that even automated agents can solve their issues. 

Improves Agent Productivity

The implementation of AI Agents within a contact center should not be viewed as taking away jobs from human workers – but rather augmenting them and enabling them to be more productive. Agentic AI Agents can directly support human teams via Agent Copilot by listening in on calls, understanding customer intent, and taking care of many of the most manual, time-intensive tasks associated with them. 

Increases Efficiency

Gone are the days of predicting every aspect of a customer’s query process to help determine the most appropriate agent response. Agentic AI allows for genuine autonomy and can operate continuously without the need for human intervention. 

This, in turn, means you’ll save time and money in your business because you can automate more complex customer queries that would have otherwise required human intervention. 

Allows for Better Personalization

Agentic AI uses both short- and long-term memory to improve customer personalization. It can remember a customer’s preference from earlier calls and utilize it, even if it’s only tangential to the current need. For example, if a customer was trying to book a flight and hadn’t requested a meal preference, but the AI Agent remembered they had asked for a vegetarian meal in the past, it could prompt the customer and ask if they’d like a vegetarian option again. 

Use Cases of Agentic AI

To demonstrate Agentic AI, you need to consider how it will work ‘in the field.’ Unlike previous versions of customer-facing AI technology, Agentic AI truly excels in more unpredictable cases where a customer’s response is not as easily defined.

Broadly speaking, Agentic AI improves all previous use cases for AI Agents, giving them more autonomy when it comes to achieving goals. Here are just a few examples…

Outbound Calling

When an AI Agent needs to make an outbound call to a customer, whether that’s to remind them of something or to try and sell a product/service, the customer’s responses are far harder to plan for. Agentic AI gives the AI Agent the ability to ‘roll with the punches,’ adapting to the customer’s responses and reasoning its way toward the core goals of the call. 

Order Tracking

Agentic AI Agents have more initiative than past iterations, making them ideal for common CX problems like order tracking. In the past, an AI Agent could speed up some parts of the order tracking process such as collecting the order number, retrieving order status, and creating case summaries – but let’s say, if the order status is “dispatched” and the customer calls to complain the package has taken to long to arrive, it would need to involve a human agent to find the shipment. 

Now, with Agentic AI, the AI Agent has the initiative to carry out every task associated with the customer’s goal, which means it can find the order number, and even autonomously contact the shipping team/warehouse to seek a resolution and then update the customer – all in the same call. 

AI2AI/AI2Human 

While enterprises can choose to have a single AI Agent for every single process, building dedicated agents for specific tasks allows you to assign different agent personas for different jobs flexibly and optimize each agent for specific tasks. Agentic AI lets you do just that, all while providing smooth Agent2Agent transfers. These agents all share a memory and can recognize the context of a customer’s problem, so users enjoy a smooth experience across the entire interaction. 

This capability also improves human agent handovers, ensuring that a customer’s call has been progressed as much as possible by the AI Agent before involving your human team – meaning they can immediately focus on the most complex issue. 

Enhanced Customer Service 

AI Agents already excel in basic, predefined areas of customer service where requirements are fairly linear. Collecting a customer’s personal details for verification, for example, is something that can already be automated efficiently with little need for iteration. 

Agentic AI, however, introduces far more task autonomy to the customer service process. With its ability to carry out tasks autonomously, Agentic AI broadens the capabilities of a customer service agent and allows them to actually accomplish more for the customer, whether that be tracking down a lost order or processing a refund – all without having to involve a human. 

Examples of Agentic AI

The use cases we’ve just covered help define where Agentic AI may drive the biggest improvements to your business, but we also want to explore a more in-depth example of how it works…

Asynchronous Problem-Solving for Retailers

A customer named Michael is frustrated by a delayed order. He calls an AI agent named Jaimy to track it down. Using Agentic AI, Jaimy takes this task and immediately begins breaking it down, first by asking Michael to provide his order number, then using an API call to check the order in the backend system. Jaimy can only find a status code that she recognizes as unhelpful to the customer. 

Previous versions of AI-driven customer support may have simply reported this code to the user without realizing how unhelpful that would be. Instead, Jaimy understands that the customer is frustrated and wants a more tangible solution for their order. 

She tells the customer she is looking into the issue further and triggers an offline AI Agent to quickly delve into the brand’s knowledge base to find out what to do. The answer is to call the warehouse in Hamburg, Germany. 

Jaimy asks the customer to hold and then makes the call – all within the same conversation without having to hang up or call back. When she contacts the warehouse, she communicates fluently in German using Cognigy.AI’s multilingual capabilities.

Finally, after resolving the issue with the German-speaking warehouse team, she returns to the customer call and switches back to English to inform them that the issue is cleared and to give them a new estimated arrival date for the package. 

What Are The Best Practices for Agentic AI?

When it comes to adopting any AI-driven automation in your business, there are best practices you need to bear in mind, including: 

Audit Your Knowledge Base & Tech Stack

Though Agentic AI Agents use LLMs to gain foundational knowledge, they work best when using data specific to your organization. You need to ensure you know where this information is (e.g. a centralized Knowledge Base, SharePoint site, etc.) and how it is stored before implementing Agentic AI.

Similarly, Agentic AI is about carrying out tasks, so it needs to be able to interact with your backend systems to function. Work with your tech team and your AI provider to audit your technology stack and determine how an AI Agent will function within it. For popular software like Salesforce, Cognigy.AI’s Agents come pre-trained and ready to carry out tasks – but bespoke tools may present challenges you need to plan for ahead of launch. Nevertheless, if it has an API, it can be integrated and used.

Design Specific Agents For Specific Roles

Building dedicated agents for specific tasks allows you to design different agent personas, meaning you can optimize each agent’s behavior and training around the specific tasks you design them for. With Agentic AI, you can do this quickly and intuitively via natural language prompts. 

Combine Agentic AI with Structured Workflows

Though the full autonomy of Agentic AI is beneficial for all of the reasons we’ve just covered, it’s also worth noting that it is still a system you can govern and control. With Cognigy.AI, you can combine some elements of a more deterministic intent-driven AI system with the autonomy of Agentic AI freely within the same flow. 

This may sound confusing, so let’s explore it further. Take, for example, a healthcare brand that has a compliance-heavy process for reporting the side effects of medication. An Agentic AI Agent can act more autonomously for the majority of calls, but you can also ensure it follows a pre-defined process whenever a customer mentions side effects. 

What is the future of Agentic AI?

Agentic AI is the future of customer-facing AI, helping tackle many of the hesitations a customer may have about AI Agents. Not only does Agentic AI make your own business more productive and cost-efficient, it also helps customers have their needs met as effectively as possible. 

For enterprise customer service operators, there’s never been a better time to explore automation. 

If you’d like to learn more about Agentic AI within our platform, read this blog. Otherwise, contact Cognigy.AI today to see how our AI agents can help you save time and money while simultaneously improving customer satisfaction. 

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.customerexperiencedive.com/news/customers-comfortable-ai-customer-service/