You’ve invested in live chat. You’ve automated the FAQs. You’ve built help centers, created workflows, and added a chatbot to your site.

But the pressure hasn’t eased.

Despite all the tools in place, the core problems remain. Your team spends hours answering the same questions. Escalations keep piling up. 

Customers switch channels and repeat themselves, hoping for a better answer. What they get instead is the same templated reply.

It’s not a lack of effort from your team. But because the tools they rely on were designed to assist, not to act.

This is where agentic AI fits in. It helps CX teams resolve issues faster, reduce handoffs, and improve service quality at scale.

In this blog, we’ll explore what agentic AI is, how it compares to generative AI, its benefits, key use cases, common challenges, and what it takes to implement it effectively. Let’s get started.

Agentic AI refers to the systems that act autonomously to achieve complex goals and workflows with limited direct human intervention. These systems understand context and instructions in natural language, set appropriate goals, reason through subtasks, and adapt decisions and actions based on changing conditions. 

When agentic AI powers interactive systems like chatbots or support platforms, they become AI agents that interact with users, understand intent, and achieve tasks independently. 

If a customer reports a missing package, the AI agent can fully resolve the complaint by checking the order status, verifying the issue, and issuing a refund. Traditional automation stops at sharing a tracking link or escalating to a human agent.

Many CX teams already use generative AI to handle tasks like drafting responses, summarizing conversations, or translating messages. These tools help agents work faster, but they still rely on humans to take the next step. Their role is limited to assisting and not making decisions or completing workflows. 

Agentic AI is different. It’s built to act. It identifies the goal behind a customer message, breaks down the steps needed to achieve it, and follows through.

Here's a head-to-head comparison highlighting their key differences:

Daniel O’Sullivan, Gartner’s Senior Director Analyst, says, “Agentic AI has emerged as a game-changer for customer service, paving the way for autonomous and low-effort customer experiences. Unlike traditional GenAI tools that simply assist users with information, agentic AI will proactively resolve service requests on behalf of customers, marking a new era in customer engagement.”

So what does this actually look like in practice? Here are some of the most tangible ways agentic AI improves the customer experience: 

Faster resolution times

Slow response times remain one of the biggest challenges in customer support. Your team spends countless hours triaging tickets and hunting for information across disconnected systems. 

Agentic AI solves this by instantly categorizing issues, retrieving data from internal systems, and completing actions like refunds or status updates, without human input. This reduces resolution time significantly and improves customer satisfaction. 

Personalized interactions

According to a McKinsey study, 71% of customers expect companies to deliver personalized interactions. And 76% get frustrated when they don’t get it. 

Traditional automation tools treat every customer the same. They rely on pre-written flows and canned responses that ignore customer history, preferences, or intent.

Agentic AI, on the other hand, pulls context from past behavior, purchase history, and recent interactions to tailor responses and actions. If a returning customer asks about pricing, it might suggest a different plan based on their actual usage or previous conversations.

Reduced operational load

Your customer support team gets frequent queries like address changes, subscription details, order updates, and password resets. Most of these aren’t complex, but they still consume time and energy. 

Agentic AI takes over repetitive workflows without needing supervision. It completes tasks end-to-end, so human agents only step in for exceptions or complex issues. This reduces ticket volume and gives your team room to focus on what matters.

Improved self-service

Salesforce research shows that 61% of customers prefer self-service to resolve simple issues. But most self-service tools don’t live up to that promise. 

Customers struggle to find relevant options, face rigid interfaces, and often give up after long wait times. What was meant to reduce effort ends up increasing frustration and ticket volume.

Agentic AI fixes this by handling full workflows directly within the conversation. Customers can update billing, reschedule appointments, or cancel services directly in the chat interface by interacting with the AI agent. 

Improved multichannel support

Customers rarely stick to one channel. They might start on chat, follow up by email, and later engage on WhatsApp. Traditional AI chatbots treat each interaction as separate, which means customers have to repeat themselves multiple times. This creates friction and frustration.

Agentic AI changes that by maintaining context across channels. It remembers past conversations, understands what’s already been discussed, and picks up where the customer left off.

Wondering how to incorporate agentic AI into your enterprise workflows? 

Here are 8 practical use cases you can start with to help your team resolve issues faster, reduce manual effort, and improve the customer experience.

Handle frequent customer inquiries

Your CX team is bogged down with repetitive queries about delivery updates, refunds, login issues, and billing. This leaves human agents with less time to focus on resolving complex issues. Agentic AI changes this. 

Here’s an example of the AI agent handling requests from start to finish. If a customer says, “I want a refund for my last order,” the AI understands the intent, pulls up the order details, and checks refund eligibility based on company policy.

If eligible, it processes the refund, confirms the action with the customer, and updates the system.

Ticket management and escalation

Agentic AI helps you categorize and prioritize incoming tickets based on urgency, complexity, and customer impact. 

For example, imagine two tickets arrive at the same time: one customer reports a software outage affecting business operations, while another asks about setting up a custom payment plan.

The AI system identifies the outage as critical, pulls relevant account details, and escalates it to the technical team with full context. Meanwhile, it categorizes the payment request, gathers the needed information, and routes it to the right team member. 

With agentic AI, this is how you can ensure both issues are addressed efficiently and appropriately.

Sentiment detection and feedback analysis

Agentic AI can understand customer emotions, such as frustration or urgency, while resolving customer complaints.

For example, if a customer writes, “This is the third time my package is delayed. What’s going on?” the AI system detects the frustration in the message and automatically flags it for priority handling or escalates it to a human agent with full context.

It can analyze thousands of such interactions at scale, identify recurring issues, and surface insights, so you can proactively improve your support experience.

Customer onboarding

You can automate end-to-end customer onboarding processes with agentic AI by creating tailored plans for each customer based on their business use cases. 

Agentic AI-powered agents execute tasks such as sending welcome messages, providing interactive product walkthroughs, promptly answering customer questions, sharing relevant product information, and conducting regular check-ins. 

This approach creates a positive first impression, saves your team valuable time, and speeds up onboarding.

Schedule meetings

Customers don’t want to fill out forms or wait for a call just to book an appointment. Agentic AI makes the process seamless by handling it directly within the chat.

When a customer wants to schedule a meeting or service, the AI checks availability from your connected calendar, offers open time slots, captures their preferred time, and confirms the booking with a follow-up message or email.

Multilingual support

If you have a global customer base, you know the challenge of providing support across multiple languages with a limited team. Relying on generic translation tools often leads to awkward phrasing, missed context, and inconsistent service.

With agentic AI in place, you can automate customer support that deliver a consistent, personalized experience in any language. The AI detects the customer’s language from the first message, switches instantly, and continues the conversation without losing business-specific context.

Capture leads from the website

You have hundreds of potential leads visiting your website every day. If you’re not engaging them instantly, you're losing high-intent prospects and revenue.

Agentic AI helps you capture and qualify leads by engaging website visitors instantly. It greets them with personalization, asks qualifying questions, distributes lead magnets, and gathers key details like name, email, phone number, and company size.

Empower customer support agents

Support reps spend a lot of time searching for answers. They jump between tools, documents, and outdated knowledge bases. This slows resolution time, increases the risk of sharing incorrect information, and impacts team productivity.

Agentic AI cuts through the noise. It understands natural language queries, retrieves the right information instantly, and gives your team exactly what they need. 

The AI system also monitors ongoing interactions and regularly updates knowledge bases with new customer insights, so your team always works with the most accurate, up-to-date information. This speeds up resolution, improves first contact resolution, and helps your team stay focused.

So far, we’ve looked at how agentic AI can improve customer experience and where it fits in. But since it’s still evolving, implementation will come with its own set of challenges. These shouldn’t be seen as blockers but as factors to plan for.

In this section, we’ll walk through the key challenges and what you can do to overcome them.

Integration complexity

For agentic AI to work, it must connect with your existing tools like CRM, databases, and support platforms. But most of the legacy systems weren’t built with AI in mind. As a result, the AI might struggle to fetch the right data and execute workflows independently.

To make it work, you need a reliable integration layer. That means building standard data formats, cleaning up fragmented APIs, and ensuring the agent can access the tools it needs. Without careful planning and execution, these complexities in connecting external systems can undermine the efficiency and responsiveness that agentic AI is meant to deliver.

Data governance

Agentic AI needs access to sensitive data like customer records, billing information, and other enterprise proprietary data to perform tasks. But when these systems connect to other applications, they can become vulnerable to security risks.

To ensure data security, you need to establish clear AI guardrails. Define what the AI can and can’t do. Set role-based permissions, restrict access to sensitive fields, and log every action the system takes. 

In high-risk workflows, keep a human in the loop—where AI agents gather context and suggest next steps, but final decisions should remain with a human.

Change management

Introducing agentic AI alters how your team operates. Some employees may fear job replacement, while others might be apprehensive about the AI's decision-making capabilities.

The key to tackling this is to involve your team from the outset. Show them how agentic AI helps with repetitive tasks and frees them up for more meaningful work. Provide hands-on training, walk them through real examples, and give them space to ask questions. 

When your team understands the value and feels part of the process, adoption becomes much smoother.

As a CX leader, you want to give customers a great experience without burning out your support team. Agentic AI helps you do both. It takes on repetitive tasks, handles full workflows, and gives your team room to focus on what matters.

pagergpt makes it easy to get started. You don’t need to code, set up heavy infrastructure, or wait months to go live. You can train your AI agent using your website content, uploading files, and connecting with your tech stack.

Here’s what pagergpt offers:

• A shared live inbox so your team can collaborate on live conversations

• Multichannel deployment to support customers in their preferred channels

• Built-in lead capture capabilities to engage visitors in real time and send qualified leads straight to your CRM

• An intuitive analytical dashboard to track AI agents’ performance, understand customer queries, and spot trends to make informed decisions. 

Ready to build your first agentic AI workflow? Try pagergpt today!

What is agentic AI?

Agentic AI refers to AI systems that act autonomously to achieve complex goals and workflows with limited direct human intervention. 

These systems understand context and instructions in natural language, set appropriate goals, reason through subtasks, and adapt decisions and actions based on changing conditions.

What are AI agentic workflows?

AI agentic workflows are end-to-end processes managed by intelligent AI agents. 

These workflows automatically collect data, analyze it in context, adapt in real-time, and execute actions across multiple systems. 

What is a real-world example of an AI agent?

In e-commerce, a customer support AI agent can answer questions about orders, process return requests, assign tickets to the right team, and schedule follow-ups. 

It works within support tools like CRMs or helpdesk software to complete these tasks automatically.

What are the use cases of agentic AI in customer support?

Agentic AI can auto-resolve common queries, manage refunds, update orders, detect sentiment, escalate issues, assist with onboarding, schedule appointments, and support multiple languages. 

This helps customer support teams to speed up resolution, personalize experiences, and consistently deliver a better customer experience at scale.