13 ways to improve support with an AI customer service agent

Today’s customers expect fast and accurate support 24/7—whether they’re just tracking an order or need to troubleshoot a complex issue. To meet these rising expectations, businesses are increasingly turning to AI customer service agents.

These virtual assistants use natural language processing (NLP) to instantly generate responses to common questions, route inquiries, and automate repetitive tasks, freeing human agents to focus on high-touch interactions. In fact, generative AI tools are becoming table stakes in customer service, with 80% of customer service teams expected to use AI by the end of 2025 to boost efficiency and customer experience (CX).

The next frontier is agentic AI—autonomous systems that perceive, reason, and use real time data to handle complex tasks from end to end—even across formerly siloed systems and channels. In this post, we’ll explore 13 powerful ways AI customer service agents are helping teams do more with less, and how they’re reshaping the support landscape for good.

What is an AI customer service agent?

An AI customer service agent is an advanced AI-powered digital assistant that's designed to handle customer interactions just like a human agent would.

Powered by large language models (LLMs) and natural language processing (NLP), AI agents for customer service can understand and respond to customers in natural language, even grasping the context, intent, and sentiment of conversations.

AI agents are pre-trained on customer service interactions, company policies, and other relevant business information. Using this knowledge base, they provide highly accurate and contextually relevant responses to any number of inquiries immediately, without getting stuck on vague, complex, or incomplete phrasing.

Agentic AI takes this a step further. Unlike generative AI tools that can only respond to customers when prompted, AI agents are autonomous, adaptive, and interoperable systems. They can work across channels and systems seamlessly, using real time data and tools dynamically to deliver personalized support and solve multi-step issues from start to finish—all with zero or minimal human intervention.

The benefits of using AI customer service agents include:

• Operational efficiency: Automating routine tasks cuts down on human workloads, enhancing productivity, efficiency, and cutting costs.

• Enhanced CX: Providing fast, tailored interactions at scale helps to improve resolution times, customer satisfaction, and retention rates.

• Scalability: AI agents are a scalable way to handle high volumes of support tickets, helping to resolve issues quickly, reduce wait times, and boost customer sentiment.

13 use cases for an AI customer service agent

AI customer service agents are a game changer for support operations. Combining conversational intelligence with autonomous task execution, they’re well-suited to handling various customer service use cases. For example, they can:

1. Automate repetitive inquiries

Up to 70% of customer inquiries revolve around simple, repetitive questions for issues like order tracking, password resets, refund policies—and AI customer service agents make short work of these. Combining conversational AI with the business-specific intelligence they’ve been trained on, AI agents can automatically resolve an estimated 80% of customer inquiries on their own at first contract (FCR).

An AI customer service agent handles a routine question in the video above.

In a world where 8 in 10 customers prefer to resolve issues without contacting support reps, AI agents are a consistent, scalable solution to repetitive inquiries, boosting operational efficiency and customer satisfaction. 

2. Automate complex workflows

Beyond basic inquiries, AI agents for customer service can handle a range of multi-step workflows from end to end, such as processing refunds, updating billing info, or creating support tickets. They do this by breaking complex tasks down into a series of smaller manageable sub-tasks, then taking action autonomously until the issue is resolved.

For example, an AI agent can troubleshoot a complex IT issue from start to finish that would typically require a human agent. By retrieving data from backend systems such as device logs using APIs, the AI agent can gather the data it needs in real time to make an informed decision. It then suggests solutions until it reaches a resolution, all within a single interaction. This helps to reduce ticket volume, streamline operations, and improve customer satisfaction.

3. Reduce resolution times with AI-human handoffs

Some scenarios still require a human touch, so AI agents are designed to detect when to escalate—especially if issues are sensitive, complex, or outside the AI’s scope. By gathering context then intelligently triaging to human reps, AI agents help to improve resolution times while allowing teams to work faster and smarter. Lyft, for example, cut its service resolution time by 87% with AI agents.

How does this work? AI agents use sentiment analysis to understand customer intent and emotion in real time, while handoff triggers are also built into the agent’s logic so customers always get the help they need. If the AI agent isn’t confident it can resolve the issue, a conversation gets tense, or it detects a pre-defined trigger—it can escalate immediately. What’s more, it can summarize the conversation so far to help human reps get up to speed quickly without missing key context.

4. Proactive issue resolution

Because AI agents are connected to backend systems, they can monitor custoemr data and identify patterns, then proactively detect and address potential issues before customers even consider reaching out to support. For example, an AI-driven system could detect repeated failed login attempts and proactively reach out to the customer, offering assistance with password recovery before frustration sets in and the user submits a support request.​

By engaging with customers before problems become real issues, AI agents reduce ticket volumes and improve customer satisfaction.​ According to Harvard Business Review, companies using proactive support strategies like AI customer service agents can see a 20–40% increase in customer retention.

5. Seamless omnichannel support

Customers expect seamless support on their preferred channels, but few companies deliver because of the fragmented, siloed tools. AI customer service agents change this.

Unlike generative AI tools and legacy systems that operate in silos, AI agents are interoperable, so they can be deployed across all channels—websites, email, SMS, chat, in-app, social, voice, even messengers like WhatsApp. This enables them to seamlessly switch channels with customers in real time without losing context, ensuring a smooth interaction no matter where it starts, while eliminating the need for customers to repeat themselves.

By eliminating the silos that slow and fragment the traditional support experience, AI customer service agents enable one unified experience so customers can enjoy consistent, context-driven care at every touchpoint—while also creating a single unified picture of customer data to improve insights and decision.

6. Improve service quality

AI agents for customer service elevate the overall quality of support interactions in a few key ways:

• Personalized support at scale: AI agents access customer profiles, behavioral data, and remember returning customers to make interactions feel more human and tailored without slowing down service.

• Consistent error-free responses: AI agents pull responses from your knowledge base or backend systems to deliver accurate answers every time.

• 24/7 availability: AI agents ensure customers can get instant quality care at all hours, regardless of time zones or holidays.

• Faster resolutions: Combining task execution with real-time data access, AI agents can resolve issues end to end, for faster resolution times and more satisfied customers.

7. Greater access and quality of insights

A hidden benefit of AI customer service agents is that they generate and integrate insights with every interaction. Unlike traditional systems that rely on after-the-fact reporting, AI agents continuously monitor, analyze, and surface valuable data in real time, such as resolution time, escalation frequency, and customer sentiment. This visibility enables support leaders to measure operational KPIs to identify bottlenecks, knowledge gaps, and improve operations with greater efficiency and certainly..

And since AI agents span all channels, they turn all that omnichannel conversation and interaction data into one unified view of customers. This comprehensive understanding of team performance and customer experience leads to better business decisions. For example, reliably monitoring the performance of AI systems to measure ROI of customer service automation.

8. Proactively guide human agents

AI agents aren’t just helpful to customers—they’re also powerful allies for support teams. By listening to interactions and working in the background, AI agents can recommend relevant content, responses, or guide next steps based on the flow of conversation. Embedded in workflows, they can even summarize previous interactions or pull in key customer data so human reps don’t have to waste time on digging for tickets.

This proactive, intelligent assistance helps to ensure customers receive the most relevant information as quickly as possible, for more accurate, consistent, timely interactions that drive customer satisfaction. In fact, one study on conversational AI showed that 41% of enterprises are using AI-powered copilots for customer service and 60% use them for IT help desks.

9. Enhance training and onboarding

In addition to empowering human reps to deliver faster, more accurate, and more empathetic service—AI agents can also serve as a mentor. For new hires, AI agents can act as an always-on training companion, providing in-the-moment assistance that accelerates onboarding. Over time, they also identify knowledge gaps and recommend targeted coaching, making continuous learning part of the workflow.

For example, AI can analyze the customer’s sentiment in real time and offer guidance on tone or phrasing. If frustration is detected, the AI might recommend empathetic language to de-escalate the situation—helping agents maintain professionalism and build trust.

10. Improve your help center

AI agents for customer service are trained on help center resources to provide accurate outputs and auto-surface resources during conversations. Beyond using the help center, they can also improve it and make it more aligned with what customers want.

By constantly analyzing interactions, they can identify content gaps and recommend new articles or updates based on new information. They can also track how often help articles are viewed or resolve the issue, and for low-performing content, suggest improvements—even drafting or revising content to help maintain tone and clarity to help teams scale content creation in a way that evolves with customers’ needs.

11. Scale localized, specialized support

It’s typically costly for global support teams to scale localized support, but AI agents change this. Rather than hiring and training a local workforce, service leaders can instead deploy a team of specialized AI agents with multilingual capabilities that are trained on region-specific knowledge and therefore sensitive to cultural nuances. It’s a scalable way to meet global customer expectations without the need for massive human resources.​

With AI agent platforms like Sendbird, it’s easy for non-technical teams to build, train, and deploy AI agents customized to different regions—but also different product lines or account settings. By building a team of AI agent specialists, enterprises can offer highly accurate, tailored, and consistent experiences across product lines and regions, helping to both reduce costs and elevate support quality.

12. Gather customer feedback

AI agents can automatically gather feedback without disrupting the customer experience, helping to improve service quality and customer sentiment to reduce churn.

For instance, AI agents can automatically trigger post-interaction surveys or prompt feedback during conversation, while also analyzing customer sentiment. They can also follow-up automatically on negative feedback by looping in human agents or routing tickets, helping to recover interactions before they impact customer sentiment and experience. They then integrate and categorize feedback so it appears as trends and actionable insights in dashboards or AI-powered workflows so teams can use it right away.

13. Automate post-support workflows

AI agents for customer service can easily handle the cascade of follow-up tasks that need to happen behind the scenes after a support interaction. This includes syncing CRM updates, sending confirmation emails, providing case summaries, scheduling future check-ins, notifying other teams—even triggering internal agentic workflows for tasks like refund processing or subscription cancellations.

By automating these repetitive but critical tasks, AI customer service agents free up your human team to focus on higher-value work, while increasing the speed, accuracy, and consistency of operations overall.

How to build an AI customer service agent

AI customer service agents have evolved beyond virtual assistants and chatbots. Today’s agentic AI systems are autonomous and adaptable, capable of managing a growing set of complex tasks across multiple channels with precision. They can also be customized to specific use cases, audiences, and workflows. But no matter the use case, the foundation of an effective AI agent starts with these three key building blocks:

1. Unified customer data

For AI agents to deliver relevant, accurate help, they need access to clean and comprehensive customer data across platforms. This includes past interactions, preferences, purchase history, and behavioral signals. Training AI agents on a clean, centralized data layer enables context-aware responses that feel seamless and personalized.

2. Memory and adaptation

Agentic AI uses a memory module to remember previous conversations and outcomes. This enables the agent to tailor future interactions, avoid repetition, and learn from engagements to improve over time. This enables experiences that are more human and relevant to a business’s unique customers.

3. Continuous optimization

AI agents require monitoring and optimization to maximize their performance. An effective support system includes an operational pipeline for training, tuning, and improving the accuracy, relevance, and user experience of AI agents. This includes feedback loops from users and agents, real-time data monitoring, and alignment with evolving business goals and policies.

Learn more: 8 steps to build an AI agent