Compare top platforms: 8 best ai customer service agents in 2026: (Updated)

Introduction

Traditional customer support bots were built around rigid decision trees, scripted flows, and keyword matching. They could answer predictable questions, but struggled when customers changed topics, provided incomplete information, or asked for something outside a predefined workflow.

Generative AI has changed what customer support automation can do. Modern AI customer service agents can understand intent and context, retrieve information from business systems, reason through multi-step requests, and take actions rather than simply generate a response.

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. Unlike traditional chatbots, an AI customer service agent can be connected to systems such as CRMs, order management platforms, billing systems, and knowledge bases. This allows it to move from answering “Where is my order?” to actually retrieving the order, checking its status, initiating an eligible action, and confirming the outcome with the customer.

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

What is an AI customer service agent?

An AI customer service agent is an AI-powered system that can understand customer requests, access relevant business information, make decisions within defined guardrails, and take actions to resolve customer issues.

Unlike traditional chatbots, which primarily retrieve information or follow predefined conversation flows, AI agents can work toward an outcome. For example, an AI agent for customer support could authenticate a customer, check an order in a CRM, determine whether a refund is eligible, initiate the refund, and confirm the result in the same conversation.

This makes AI agents particularly useful for repetitive but multi-step customer service workflows where the system needs to combine customer context, business rules, knowledge, and actions.

The core capabilities typically include:

• Intent and context understanding

• Knowledge retrieval

• Reasoning and decision-making

• Tool and API use

• Workflow execution

• Human escalation

• Conversation memory

• Performance monitoring and quality assurance

What to look for in an AI customer service agent?

1. It understands intent, context, and customer history

A good AI agent for customer support should understand what the customer is trying to accomplish, not simply identify keywords in a message. It should account for the customer's previous interactions, current conversation, account information, and relevant business rules before generating a response or taking an action.
For example, “I was charged again even though I cancelled last week” requires the system to understand billing, cancellation status, account history, and potentially refund eligibility. A useful AI agent should be able to retrieve that context and determine the appropriate next step rather than simply return a billing FAQ.

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. It can take action, not just answer questions

The biggest difference between an AI customer service agent and a conventional chatbot is what happens after the response. A chatbot may explain a refund policy. An AI agent can check whether the customer qualifies, retrieve the order, initiate the refund, update the support record, and confirm completion.

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. It works across channels without losing context

Customers do not think in channels. They think in problems. A customer may start with chat, move to voice, receive an email confirmation, and later contact support again. A strong AI customer service platform should preserve relevant context across those interactions rather than forcing the customer to start over.

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.

4. It can be monitored, tested, and improved after deployment

An AI agent should not be treated as a “set it and forget it” chatbot. Enterprises need visibility into accuracy, resolution rates, escalations, hallucinations, policy adherence, customer sentiment, and failure modes.

Look for platforms that can automatically evaluate conversations, run simulated test scenarios, identify problematic responses, and feed those insights back into the AI workflow.

Lets compare 8 AI customer service agent platforms

1. Level AI: Best for unified AI-powered customer service and CX intelligence

Level AI combines AI-powered customer conversations with conversation intelligence, quality assurance, and customer experience analytics. Its Virtual Agent can handle voice and chat interactions, retrieve information, execute actions, and escalate conversations to human agents with context.

Best for: Enterprise contact centers that want AI automation alongside visibility into human and AI interactions.

Key capabilities:

• Autonomous resolution across voice and chat

• Context-aware actions through connected systems

• AI-powered QA and response evaluation

• Conversation and customer sentiment analytics

• Human handoff with conversation context

• Identification of repetitive workflows that can be automated

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.

In customer deployments, Level AI reports improvements in CSAT and abandonment through more contextual, empathetic conversations.

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.

This is where agentic AI differs from conventional generative AI. Generative AI can produce an answer, while an agentic system can determine what needs to happen next, use connected tools, execute the required actions, and verify the outcome within defined guardrails. 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.

Turn repetitive customer requests into automated resolutions

When your support team spends too much time answering the same questions, searching for information, and handling routine workflows, AI can take more of that work off their plate. Level AI’s AI customer service agents understand customer intent, access relevant context, and take action across connected systems while escalating complex conversations to human agents.

Schedule a demo

Turn repetitive customer requests into automated resolutions

When your support team spends too much time answering the same questions, searching for information, and handling routine workflows, AI can take more of that work off their plate. Level AI’s AI customer service agents understand customer intent, access relevant context, and take action across connected systems while escalating complex conversations to human agents.

Schedule a demo

2. Zendesk

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): AI support agent

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: AI customer service platform

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: AI customer service software

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): AI customer platform

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.

7. Quiq: Agentic AI for CX

Quiq improves customer engagement with its AI-powered platform, enabling businesses to connect with customers across multiple channels, including SMS, web chat, and social messaging apps. Designed to enhance both customer and agent experiences, Quiq combines
advanced AI with human expertise to deliver fast, accurate, and
personalized interactions.

Key features include:

• AI Agents: Automate routine inquiries and provide instant responses,
  reducing wait times and improving customer satisfaction.

• Omnichannel Messaging: Seamlessly manage conversations across
  platforms like WhatsApp, Facebook Messenger, and more, all from a single interface.

• Agent Assist: Equip agents with real-time AI-driven suggestions and
  insights to handle complex queries efficiently.

• Generative AI for Content Creation: Automatically draft responses and
  summaries, saving agents time and ensuring consistent communication.

• Integration-Friendly: Easily integrates with popular CRM and
  customer support tools, streamlining workflows and enhancing productivity.

Quiq’s platform empowers businesses to scale their customer support
operations while maintaining a personal touch, making it a standout choice for modern contact centers.

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

8. Sierra: Agentic AI for customer service

Sierra provides AI agents designed to handle customer interactions across voice, chat, email, WhatsApp, and other channels. Its agents can connect to systems such as CRM and order-management platforms to complete multi-step customer-service tasks.

Key features include:

• Omnichannel AI agents

• Multi-step task execution

• CRM and backend integrations

• Voice and digital support

• Customer-context management

• Enterprise guardrails and governance

Best for: Enterprises looking for AI agents that can move beyond conversational support into end-to-end task completion.

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.

How companies orchestrate multiple AI agents for customer service

As customer service automation becomes more sophisticated, companies are moving beyond a single AI agent handling every task. Instead, multiple specialized agents can work together under an orchestration layer.

For example, a customer request could trigger an authentication agent first. Once the customer is verified, a routing agent can determine the intent and send the request to a billing, returns, technical support, or account-management agent. A knowledge agent can retrieve relevant policies while an action agent interacts with the CRM or backend system. If the request falls outside predefined rules, the system can escalate it to a human agent with the full conversation context.

The orchestration layer is responsible for deciding which agent should act, what information it can access, what actions it is authorized to take, and when a human needs to intervene.

How to choose an AI agent vendor for enterprise customer support

Enterprise buyers should evaluate AI customer service platforms on more than conversation quality. A convincing demo does not necessarily mean the system will perform reliably in production.

Evaluate vendors across six areas:

1. Resolution capability: Can the agent actually complete tasks or only answer questions?

2. Integration depth: Can it securely connect to CRM, help desk, billing, order management, and other systems?

3. Accuracy and grounding: How does the platform prevent hallucinations and ensure responses are based on approved information?

4. Governance: Can administrators define permissions, escalation rules, guardrails, audit trails, and human approval requirements?

5. Quality measurement: Can the vendor continuously evaluate AI interactions rather than relying on occasional manual reviews?

6. Economics: Does pricing align with outcomes, resolutions, conversations, usage, or seats, and can costs remain predictable as automation scales?

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