Top 6 AI Customer Service Agent Platforms for Automated Support

Key Takeaways

• AI customer service agents understand intent and context in real time, enabling faster and more accurate customer support
• AI agents for customer service automate workflows like ticket resolution and customer support automation, reducing agent workload
• Omnichannel AI support ensures consistent experiences across chat, voice, email, and contact centers
• AI in customer service improves efficiency, lowers costs, and enables scalable 24/7 support with AI support agents
• Level AI enhances AI customer service agents with conversation intelligence and quality insights, helping contact centers improve performance and customer experience

Introduction

Until recently, this was more a promise than a reality. Traditional customer support bots relied on rigid decision trees that forced customers through scripted menus, often frustrating users when their needs didn’t fit into predefined categories. You’ve probably seen it: “I see you have a question about returns. Press 1 for yes, 2 for no.”

But generative AI has changed the game. Modern AI customer service agents can now understand language more like a human, holding natural conversations, recognizing intent, and even responding with empathy when customers are confused or upset.

As a result, companies are rethinking how they use automation in customer service, and many are now actively searching for the right AI agent to deploy.

That said, not all AI agents are created equal. So in this article, we discuss what to look for when evaluating these platforms.

We then cover some popular AI customer service agents, starting with a deep dive into Level AI’s own Virtual Agent, built on our advanced conversational intelligence and automated QA technology:

1. Level AI Virtual Agent
2. Zendesk AI Agents
3. Fin (by Intercom)
4. Sendbird
5. Ada
6. Breeze (by HubSpot)

What to look for in an AI customer service agent?

1. It delivers fluid and human-like conversations

Make sure the agent you’re considering uses real AI (not rule-based logic) to interact with customers. When conversations feel natural and responsive, customers are more likely to feel heard and understood, and get a better experience.

Unlike traditional bots that rely on decision trees or scripted menus, true AI-powered agents use NLP and generative AI to understand intent, detect sentiment, and respond appropriately, even when the conversation doesn’t follow a predictable path. Such contact center automation tools can handle interruptions, switch topics midstream, and maintain a conversational tone that adapts to the customer’s mood and phrasing.

Many legacy bots try to mimic understanding by using rigid flows and keyword matching. But this often backfires. For example, if a customer says, “I wasn’t expecting to pay that much. Is there anything you can do?” a basic bot may not register this as a billing concern simply because it doesn’t contain the exact keywords like “refund” or “return.”

This lack of contextual awareness is also why traditional systems tend to force customers into choosing from preset menus, an approach that feels unnatural and often frustrating. When people have to repeat themselves or rephrase to get the bot to understand, trust in the system erodes quickly, and the likelihood of escalation increases.

2. Look for a system that turns insights Into better service

In customer experience strategy, closing the loop means not just collecting feedback, but acting on it, and letting the customer know you did. For example, if a customer complains about a confusing ordering process, closing the loop means acknowledging the issue, addressing the root cause, and improving the process behind the scenes. This builds trust while also driving meaningful change within the organization.

The faster your system can react to feedback, the more effectively it connects insights, automation, and learning, forming a continuous cycle of improvement. That’s where many legacy chatbots fall short. Bots built on decision trees don’t adapt on their own. Their responses are hardcoded, so any changes require manual updates and developer involvement, which is both time-consuming and costly.

In contrast, AI-driven virtual agents use customer analytics software to learn from real interactions. They can interpret intent, manage edge cases, and refine their behavior over time. By analyzing patterns in customer feedback, they can proactively identify recurring issues and optimize future responses without human intervention.

The most capable agents go even further. They turn insights into action, updating records, adjusting orders, or triggering follow-ups, all while tracking performance metrics like resolution rates, customer satisfaction, and others that support a wide range of customer analytics use cases. This kind of intelligent feedback loop not only improves service quality but also helps your team stay ahead of customer needs.

3. Chatbots should function just as well on voice as in text

Today’s customers expect a smooth, consistent experience across every channel, whether that’s web, chat, email, social, or voice. But many platforms still struggle to deliver this, especially when it comes to voice. That’s because voice is harder to get right. Conversations often feel robotic or scripted, which makes sense given that many legacy bots rely on rigid decision trees and keyword matching.

AI-driven agents, by contrast, are built to handle real conversations, not just recognize commands. They’re designed to understand natural language, respond with empathy, and take meaningful actions across both voice and text channels. And they can do this at scale, adapting fluidly to the customer’s intent regardless of where the interaction begins.

Next, we’ll highlight the top AI agent platforms that combine conversational intelligence with strong, channel-agnostic performance, offering a consistent experience whether customers are typing or talking.

Top AI Agents for Customer Service

1. Level AI Virtual Agent

Level AI’s Virtual Agent is a fully integrated platform that combines voice and chat support, agentic automation, and performance monitoring in a single system. It’s designed not just to talk, but to understand, take action, and continuously improve.

AI Virtual Agent:

• Uses natural language understanding and semantic intelligence to interpret intent, tone, and context for natural, helpful conversations.
• Resolves issues autonomously and analyzes 100% of interactions to uncover sentiment trends and root causes.
• Takes context-aware actions on behalf of agents and customers, with real-time tracking and full transparency
• Deploys in days with minimal engineering support and at over half the cost of traditional solutions.
• Automatically identifies and maps high-volume, repeatable queries for automation, freeing agents for complex tasks.

Below, we look at AI Virtual Agent’s main features in more detail:

1. Conversations That Feel Human, Not Scripted

Legacy chatbots often feel robotic because they rely on rigid scripts and siloed systems. Different channels typically run on separate technology, so the bot handling web chats may not share training data with the one managing social media or voice calls. This limits what each bot can learn and makes it harder to respond with the context customers expect.

These disconnected systems also create inconsistent experiences. A voice bot might sound overly formal while a chat assistant feels casual or off-brand. Customers may also have to repeat their issue when switching channels, which leads to frustration and lower containment rates.

AI Virtual Agent’s DialogIQ solves this by unifying voice and chat under a single intelligence layer that shares context, training data, and customer history across channels. This allows conversations to stay consistent and continue smoothly, no matter where they begin.

Behind the scenes, Level AI’s sentiment detection recognizes a wide range of emotions, including anger, annoyance, disappointment, worry, admiration, happiness, and gratitude. The agent can adjust its responses in real time, offering empathy when someone is upset, reassurance when they’re concerned, or enthusiasm when they’re pleased. It can also handle interruptions and topic shifts more naturally, keeping conversations on track.

Level AI’s human-like dialog delivers consistent, natural conversations across multiple channels and maintains a unified customer experience despite high volumes of customer interactions.

Compared to other offerings, our Virtual Agent’s human-like and empathetic interactions help it achieve a 30% higher CSAT and 50% lower abandonment rate.

2. Actionable AI that Responds, Executes & Resolves in One Flow

Many chatbots only inform but don’t necessarily resolve, answering FAQs but not fixing issues.

Many also lack native integrations with mainstream contact center software and need engineering support to connect with backend systems. Beyond adding complexity and cost, this lack of integration can also lead to hallucinations, as the system isn’t grounded in real-time business data or system logic, which creates brand risk.

Level AI’s AgentIQ gives the software the ability to take real action, not just respond. It can reason through complex situations, plan multi-step tasks, and execute them autonomously with better response times, handling both informational and transactional use cases within a single interaction.

It integrates readily with your existing tech stack and with products like Salesforce, Zendesk, and HubSpot, to do things like:

• Directly modify orders
• Update records
• Fetch account data
• Generate support tickets
• Send notifications

After taking an action, it autonomously sends follow-up communications on its own, and its answers are strictly based on the provided knowledge sources. Because it can take the right actions during a conversation, it solves issues without needing a human, leading to 3x better containment.

It’s easy to set up because it doesn’t require any coding. You specify skills, which are discrete tasks you want the virtual agent to handle on its own.

Let’s say you work at an e-commerce company and want the AI to handle order status requests. You’d start by setting up an “Order Status” skill:

First, you’d define what triggers the virtual agent to activate that skill, followed by specific instructions. From there, you define exactly how the bot should address customers and what kinds of information it should ask them:

Next, you specify actions for the bot to take, like “Fetch Order Details” or “Fetch User Details.”

You can then choose to connect external systems (via APIs) to each action, such as connecting the action “Fetch Order Details” to your CRM, allowing the platform to retrieve order details for the specific customer.

Its agentic setup lets you add other information like knowledge sources (e.g., PDFs of refund policies and delivery times) to increase the agent’s accuracy. It also allows you to define further use cases, common mistakes to avoid, and guardrails so the agent stays on track.

When you’re ready to go live, Level AI gives you customization options like JavaScript code snippets for embedding the agent in a website. You can also specify brand look and feel, such as colors, logo, etc.

3. Turning Every Conversation Into Insight

One major challenge with traditional chatbots is the lack of visibility into key performance metrics.

CX leaders often have to dig into individual conversations just to understand things like customer satisfaction (CSAT), resolution rates, or whether the chatbot is even following brand standards. There’s little built-in tracking for metrics like abandonment rates, escalations, or overall quality of service.

This lack of clarity happens because most platforms don’t offer strong tools for automatically evaluating QA. There’s also no easy way to monitor the chatbot’s behavior or catch serious issues like hallucinations or misinterpreted requests. These are high-stakes situations: if the chatbot gives a wrong answer or confuses a request, trust issues with customers might develop.

AI Virtual Agent’s quality review provides ongoing monitoring of the quality and performance of all conversations, both from human agents and AI. It doesn’t just look at real-time chats, but also reviews past interactions to help teams close the loop and drive constant improvement. Quality review acts as an AI evaluator, tracking every response your AI agent gives and reporting on outcomes like response times, resolution rates, escalation rates, and customer sentiment.

It includes a built-in testing framework that runs hundreds of simulated conversations through our artificial intelligence software and call quality monitoring tools to ensure it responds accurately and appropriately in different situations. It also integrates with analytics tools like Tableau, Domo, and Looker, so teams can track performance in a way that fits into their existing reporting workflows.

To maintain consistent quality, the virtual agent uses Level AI’s AutoQA to score AI responses based on clear rubrics. It even maps the full customer journey, helping teams understand how all the touchpoints, like chat, voice, and email, fit together and affect the customer’s experience.

At the heart of this system is our Voice of the Customer Insights that analyzes real conversations to uncover hidden issues and surface recurring problems to spot opportunities for improving both human agent and AI workflows.

Level AI’s Virtual Agent also uses a proprietary scoring system called iCSAT. Unlike traditional satisfaction scores, iCSAT combines sentiment, effort, and resolution data to offer a full picture of how the customer felt during the interaction. Measured on a scale from 1 to 5, it shows not only how well the agent is performing, but also where customer needs are going unmet or where frustration is building up.

See our latest article on how to improve quality assurance in a call center.

By analyzing every interaction, Virtual Agent’s EnlightIQ spots tasks that show up frequently and could be handled by the AI, reducing the burden on human agents. And when a customer does need to talk to a person, EnlightIQ uses artificial intelligence to detect that intent and pass along the full conversation history for a frictionless handoff.

All of this creates a complete, closed-loop system that not only tracks and understands performance but also uses that information to improve the AI Virtual Agent and increase call center efficiency over time.

To see for yourself how AI Virtual Agent delivers human-like customer service and real-time action by combining voice, chat, and intelligent automation in one AI agent, schedule a free demo today.

2. Zendesk AI Agents

Zendesk AI Agents are chatbots that resolve customer requests across multiple channels and handle routine inquiries from routine FAQs to complex issues. These AI agents determine why the customer is contacting the organization and can retrieve accurate answers, do certain actions, and escalate to humans when needed.

Key features include:

• Generative AI replies from connected knowledge sources via messaging and email
• Support for multiple languages
• Scripted and hybrid AI conversation flows
• API integrations with third-party tools
• Analytics, journey mapping, and performance dashboard

Zendesk AI Agents is offered as a feature of their standard pricing plans, starting at around $50 for a small customer service team.

3. Fin (by Intercom)

Fin is designed to answer requests and resolve queries across channels with conversational interactions. It uses generative AI and integrates with external services like helpdesks and knowledge bases.

Its features include:

• Delivers natural, personalized responses and handles complex issues using conversational AI
• Draws from customer data in multiple sources to generate complete answers
• Routes unresolved or complex cases to human agents
• Works across website, email, and messaging
• Provides analytics, workflow automation, and integrates with existing support operations

Pricing starts at around one dollar per resolution, with a minimum allotment of 50 resolutions per month.

4. Sendbird

Sendbird is a multichannel AI agent that handles customer inquiries and focuses on smooth handoffs to human agents when required. Sendbird integrates with a number of external customer data systems like CRMs, helpdesks, etc., and offers security and compliance with several standards like GDPR, HIPAA, and more.

Key features include:

• Omnichannel support, including web, mobile, messaging, and more
• A no-code builder for creating and training bots
• Live agent handoff
• Customizable workflows that can be automated
• Personalized bot appearance
• Real-time analytics, including actionable insights and audience segmentation

Pricing isn’t immediately available on the website and requires a conversation with sales.

5. Ada

Ada is designed to automate customer service across web, mobile, and messaging channels, allowing businesses to provide instant and personalized support.

Features include:

• A no-code builder and drag-and-drop interface for designing conversation flows
• A proprietary reasoning engine combining different AI models for increased accuracy in conversational AI
• Connects with CRMs to personalize responses to individual customers
• An analytics dashboard for comprehensive reporting on interactions, performance, and customer sentiment

According to the website, you need to book a demo to get pricing information.

6. Breeze Agents (by HubSpot)

Breeze Agents is HubSpot’s AI agent that handles high-volume conversations across multiple channels. Breeze connects with external systems like your knowledge base and Hubspot CRM to provide fast, accurate, and cited responses using customer data, and can escalate to human reps when needed.

Key features include:

• Easy setup with no coding required
• Breeze copilot for assisting in tasks like updating your CRM or editing documents
• Full integration with the rest of the HubSpot ecosystem

Breeze is included as a feature in HubSpot’s Professional and Enterprise plans, and HubSpot uses a credit system to track pricing for AI usage.

What are the use cases of AI customer service agents?

1. Financial Services

• Virtual agents handle account queries, transaction disputes, and balance checks, resolving routine contacts without human handoff. McKinsey estimates generative AI could reduce human-serviced contacts by up to 50% in banking.
• A European bank deployed a gen AI-powered chatbot in its contact center that, within seven weeks, eliminated wait times for around 20% of contact center requests.
• At a separate bank, a gen AI agent now drafts credit-risk memos, increasing revenue per relationship manager by 20%.
• AI agents handle KYC by prepopulating forms, validating document uploads, and following up on missing information without agent involvement.

2. Telecommunication

• A European telecom used AI agents to cut service call resolution time by 60% and save more than a million euros annually, while also improving its net promoter score.
• A leading energy company reduced billing call volume by around 20% and cut up to 60 seconds from customer authentication by integrating an AI voice assistant into its back-end call workflow.
• A European media and telecom company deployed a gen AI copilot to give customer service agents faster knowledge retrieval during live calls.

3. Retail / Consumer Goods

• AI virtual agents handle order status, returns, and product queries at volume, with escalation paths to human agents for complaints.
• McKinsey's European Customer Operations roundtable found retail beginning to follow banking and telecom in AI adoption, with human agents shifting toward customer success roles focused on high-value buyers.

4. Cross-Industry (agent assist)

• Gartner ranks agent assist tools among the four highest-value AI use cases in customer service. These tools surface knowledge base answers, next-best action recommendations, and real-time data during live calls.

Gartner rates case summarization and post-interaction wrap-up as among the most practical use cases available. Both give agents a structured overview of each interaction without manual note-taking.

Wrapping Up: What Level AI customer service agent can really do?

Level AI’s Virtual Agent goes beyond scripted responses and adapts to real-world support needs by understanding intent and automating routine tasks across chat and voice.

Schedule a free demo with our team to see how we can help you optimize resolution rates, reduce escalations, and deliver consistent support across every channel.

Frequently Asked Questions.

1. What are AI customer service agents?

A. AI customer service agents are AI-powered systems that automate customer support by understanding queries, responding conversationally, and resolving issues across channels.

2. How do AI agents for customer service work?

A. AI agents use natural language processing (NLP), machine learning, and integrations with CRM and support tools to handle customer queries and automate workflows.

3. What is the difference between AI chatbots and AI agents?

A. AI chatbots follow predefined scripts, while AI agents for customer service can understand context, handle complex conversations, and take real actions.

4. What are the benefits of using AI in customer service?

A. AI in customer service improves response time, reduces costs, enables 24/7 support, and enhances customer satisfaction.

5. Where are AI customer service agents used?

A. AI agents are widely used in call centers, contact centers, SaaS companies, e-commerce, banking, and telecom industries.