Compare Best 5 AI Agents in Customer Service in 2025

AI agents powered by large language models (LLMs) can respond to customer queries in natural language, interpret context, and generate human-like responses. These agents can process and synthesize large volumes of information from sources such as knowledge bases.

Based on user evaluations here are the best AI agents for customer service (a higher rating is better):

Ratings represent reviewers’ overall satisfaction with each feature and do not necessarily consider the breadth of individual product features. See rating methodology. See the description for parameters.

Before diving into a deeper analysis of each framework (below), here is my high-level overview of each one and its potential suitability for your specific use case:

• Intercom’s Fin: An agentic AI copilot for enterprise contact center automation, powered by Anthropic’s Claude.

• Kore.AI Agent: An enterprise-level Conversational AI platform for contact center automation. It offers smart routing, real-time agent help, and automated QA for rental, finance, and healthcare sectors.

• Microsoft Copilot AI: A general-purpose agent that can be used for streamlining customer service operations. Copilot uses GPT-4. The AI responses are more contextually relevant than traditional solutions like Copilot Studio. Sometimes struggles with complex wording.

• IBM watsonx Assistant: Best for handling complex customer queries and support tickets. Specific use cases include customer support, HR, and marketing operations.

• Genesys Agent Copilot: Best for contact center operations. Offers inbound agents (who handle incoming customer calls), outbound agents (who make calls to customers), sales agents, and support agents.

Read more: Enterprise AI agents, Agentic AI, AI agent builders, large action models (LAMs).

Real-life AI agent examples in customer service

• Ema’s Customer Support Agent: Envoy integrates Ema’s agent for in-app customer support 70% -80% of the support team’s time
• Bland.ai: Bland.ai’s agent answers questions as a property manager to handle lease renewals and calls
• Ada AI Agent: Wealthsimple uses Ada to manage 10 FTE workload
• IBM Watson Assistant: Humana deployed Watson to manage healthcare inquiries, reducing response times by 60%
• Beam AI’s Customer Service Agent: Avi Medical automates healthcare service, reducing median response times by ~85%
• Sierra: WeightWatchers achieves ~70% resolution rate with Sierra

>Leading examples of AI agents in customer service

Fin is ideal for companies with repetitive support tickets or routine questions. Intercom Fin:

• Answers complex questions across channels
• Uses multi-source generative answers to construct more thorough responses
• Adapts to support the team’s tone of voice
• Implements support policies when needed
• Personalizes answers with contextual customer information
• Takes action in external systems

Pros:

• Simple setup, no technical skills needed (custom actions are optional).
• High answer quality with AI-generated responses.

Cons:

• Expensive pricing at $0.99 per resolution, which rises as the AI improves.
• Alternatives, like the Intercom AI Agent app, charge $0.10 per conversation.

Kore.AI Agent

Kore.ai’s Agent enhances agent efficiency with generative AI by automating workflows and offering real-time guidance:

• Next-best action suggestions to improve interactions and outcomes.
• Real-time adaptive coaching to enhance support representative’s performance.
• Guided playbooks to support reps to follow best practices for compliant service.

Pros:

• The platform requires minimal knowledge of NLP and LLM needed to configure bots.
• Kore.ai provides extensive customization options through its SDK.
• Kore.ai is well-suited for enterprises, with out-of-the-box solutions for IT tasks (like ServiceNow integration).

Cons:

• The platform’s NLU may struggle with handling highly variable user inputs. A zero-shot learning approach is recommended to improve its ability to process unknown inputs more flexibly.
• While the platform offers customization through its SDK, it is difficult to create custom solutions.

Microsoft Copilot

Copilot is suited for creating basic agents or chatbots for specific use cases like customer service. You can set up the agent with SharePoint integration with minimal technical input.

To fully leverage Copilot AI Agents, users are required to purchase Copilot licenses.

Pros:

• Since it’s built within Microsoft’s ecosystem, Copilot integrates seamlessly with other Microsoft tools like Word, Excel, and SharePoint
• Copilot Agents can pull data directly from SharePoint, making it easier to create bots that answer specific queries based on the organization’s existing knowledge base.

Cons:

• There are no easy ways to restrict or manage who can create bots, leading to potential duplication of efforts or uncontrolled sharing.
• Copilot sometimes struggles with deep or complex wording, leading to errors during bot creation or when responding.

IBM watsonx Assistant

IBM watsonx Assistant seamlessly integrates with platforms like Genesys, Nice inContact, Twilio, or your own agent system. It is designed to hand off issues to a human agent when the virtual assistant cannot resolve a query. It uses IBM large language models.

Pros:

• It can use other models like Llama2.
• Seamlessly integrates with CRMs, and communication platforms (Genesys, Twilio, Nice inContact).

Cons:

• Sometimes it generates repeated phrases.
• Long response times (15-20 seconds) and no real-time streaming for answers

Genesys Agent Copilot

Genesys Agent Copilot enhances the contact center reps by providing AI-powered guidance throughout and after customer interactions. It identifies customer intent, automatically retrieves relevant knowledge, and directs agents on the most appropriate next steps.

Key features:

• Capturing agent suggestions on knowledge improvements
• Transcribing conversations
• Providing custom scripting
• Presenting workflow process document
• Suggesting wrap-up codes
• Writing a summary of the interaction

Pros:

• After an interaction, the generated summary can be reviewed, edited, and used as part of the interaction notes.
• By automating parts of the process such as knowledge lookup, script generation, and wrap-up code prediction, the platform significantly reduces average handle time (AHT)

Cons:

• It is difficult to integrate Genesys Cloud Agent Copilot with CRMs other than Genesys or contact center systems.

>Other examples of AI agents in customer service

Ema’s Customer Support Agent

Source: Ema

Ema’s agent supports enterprise-wide actions with 100+ LLM models including GPT4o, Gemini 1.5, Mistral, and Llama 3, user can also bring their own LLM model to the platform.

• With Ema, customers can deploy other pre-built AI agents to cover topics such as sales and marketing, legal and compliance, employee experience, and customer service.
• Common use cases include approving medical procedures, adjusting insurance claims, and drafting business proposals. 
• The platform offers SOC2, HIPAA, GDPR, and ISO27001 certifications. 

Salesforce Einstein Copilot

Source: Salesforce

Salesforce Einstein Copilot is natively embedded across Salesforce applications, it can answer queries, develop content, and dynamically automate action.

Customer service tools can use Einstein Copilot to query historical customer transcripts to determine customer sentiment and build follow-up emails based on previous calls.

Bland.ai

Bland.ai is an enterprise customer service platform for AI phone calls. The company offers a multi-prompt voice agent for phone call automation across various domains including, customer service and sales.

Users can also fine-tune a custom language model for your enterprise, using prior conversation data.

Ada AI Agent

Ada is an enterprise-wide AI-powered customer service agent that enables businesses to automatically resolve service issues—across channels and languages—with the least amount of labor. 

Ada AI Agent:

• Performs actions in 1000s of apps and databases.
• Ensures each answer is grounded in your knowledge base.
• Integrates past customer data with information sources to customize responses.

Key differences between chatbots and AI agents

Chatbots traditionally operate on rigid, rules-based systems, using decision trees and pre-scripted responses to simulate conversations. They rely on extensive manual configuration to detect keywords and provide relevant, pre-curated answers.

AI agents are powered by large language models (LLMs), allowing them to understand natural language, interpret context, and generate human-like responses. These agents can process and synthesize large volumes of information from sources such as knowledge bases.

AI agents also offer:

• Knowledge integrations (syncing with systems such as Zendesk).
• Generative actions (the capacity to act on behalf of the customer).
• Reasoning (the ability to review how the resolution engine determined what to do next).
• Guidance (telling your AI how to do a specific task).
• Automated resolution insights (the rate at which the AI agents resolve issues without escalation to human agents).

How do AI agents enhance customer service?

1. Improve efficiency and speed

AI agents can handle repetitive, time-consuming tasks quickly, such as answering frequently asked questions or processing simple requests.

2. Free up humans

AI agents free up humans to focus on the “human aspect” of service. With AI agents reducing repetitive tasks, customer service representatives can focus on more creative actions, such as offering to resolve complex problems and fostering personal relationships.

3. Help control costs

By automating routine tasks, AI agents help reduce the operational costs associated with maintaining large customer service teams, leading to significant cost savings over time. Gartner predicts that conversational AI will reduce contact center agent labor costs by $80 billion in 2026.

4. Enhance customer insights

AI agents not only interact with customers but also gather and analyze data from each interaction. AI agents can contribute to better customer insights:

• Connect to the company’s knowledge base, pulling relevant data to provide personalized solutions
• Analyze customer feedback, including sentiment and satisfaction levels from surveys or interactions
• Identify emerging trends or common inquiries

5. Provide 24/7 support

Human customer service agents are bound by work hours, leading to limited customer availability. AI agents can provide support round-the-clock, ensuring customers from different time zones can receive instant assistance without waiting for business hours.

Rating methodology

The rating represents the collective voice of real software users, not the opinion of a single analyst.

• Data sources: Ratings are based on user reviews shared in G2 and third-party data. Data was collected through November 2024.
• Rating: AI agents are sorted based on # of Reviews, user satisfaction score, and market presence (market presence is based on data for both products and their corresponding vendors, with increased weight given to product-specific metrics).
• Inclusion criteria: AI agents with 10+ reviews are included.

Description for parameters

• Personalization: Refers to the AI agent’s ability to tailor interactions and responses based on individual user data, preferences, and past interactions, making the experience feel more human and customized.
• Route to Human: Indicates the AI agent is more capable of handling simple, repetitive tasks while leveraging human agents for more complex, personalized support.
• NLU (Natural Language Understanding): Indicates that the AI agent is more capable of handling diverse and complex conversations, making it more effective at assisting users.

Further reading

External Links