Compare Best 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.

We have compared four highly recommended customer service AI agents by establishing a benchmark on an imaginary company’s customer service agent. The details of the methodology are below.

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

• Tidio’s Lyro: A conversational AI platform for customer service. It uses only your support content and is powered by Anthropic’s Claude and Tidio’s in-house models.

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

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

• 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.

>Leading examples of AI agents in customer service

Tidio Lyro

Tidio Lyro is an AI-driven customer service agent created to streamline support across various channels while delivering conversations that feel human. It utilizes Claude (from Anthropic AI) alongside Tidio’s proprietary models. Tidio Lyro:

• Resolves up to 70% of customer inquiries automatically across live chat, email, and social media channels
• Integrates with existing help desk platforms through Lyro Connect
• Performs specific tasks like checking order status, creating tickets, and making product recommendations
• Uses knowledge from your website and support content to minimize hallucinations

Pros:

• Fast implementation with minimal setup required (under 5 minutes)
• Comprehensive analytics dashboard to track performance and customer interactions
• Omnichannel integration (website widget, email, Instagram, Facebook, WhatsApp)
• Multilingual support with no need to translate knowledge base content

Cons:

• Limited free tier with only 50 conversations before requiring a paid plan
• Not specialized yet for certain industries (medical and finance-related)

Tidio Lyro offers an effective combination of affordability and enterprise-level features, making it an excellent choice for small to medium-sized businesses that want to scale their customer support operations efficiently. The cost per conversation starts at $0.50, and custom pricing options are available for enterprise clients..

Microsoft Azure AI Chatbot

The Azure AI Chatbot allows you to build anything from a basic FAQ bot to an advanced multi-modal, retrieval-augmented assistant. Rather than using per-user licenses, it operates on a pay-as-you-go or reserved-capacity basis; you pay for Bot Service traffic, OpenAI tokens, Cognitive Search queries, and any additional Azure resources the bot utilizes.

Pros

• Access the Bot Framework SDK, orchestration workflows, and serverless functions.
• Integrate with Cognitive Search, Speech, Vision, Language, and other Azure services using one-click hooks to enhance voice and image comprehension or RAG.
• Publish your bot once to make it available on Teams, web, mobile apps, Slack, Facebook Messenger, and more through standard and premium channels.
• SharePoint libraries allow the chatbot to provide organisation-specific Q&A grounded in that index, similar to Copilot.

Cons

• Teams typically require developers skilled in the Bot Framework, prompt engineering, and Azure DevOps pipelines, resulting in slower onboarding.
• You need to crawl SharePoint content into Cognitive Search and set up either scheduled sync or Logic Apps.
• Sudden increases in token usage, search queries, or premium-channel traffic can surpass budget limits if quota and cost-management alerts are not closely monitored.
• RBAC limitations offer some help, but without centralized governance, overlapping bots and fragmented user experiences are possible.

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

Intercom’s Fin

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.

>Other examples of AI agents in customer service

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.

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.

Ema’s Customer Support Agent

Source: Ema¹

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: Salesforce²

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.

It can be used in various sales operations procedures for handling:

• Standard order processing
• Inventory inquiries
• Billing inquiries
• Basic returns and exchanges

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. Ada can be expensive ($1-$3.50/ticket resolution).

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.

My AskAI

My AskAI is an AI assistant for support teams, it is a cost-effective option.

My AskAI integrates with Zendesk, offering similar functionality (and even more in some areas, such as enhanced knowledge integrations, better insights, and knowledge improvement features), while being 2-10x more affordable than solutions like Ada AI agents or Zendesk AI agents.

Customer service AI agent benchmark methodology

We have evaluated four industry leaders on their API keys or playgrounds with the hold-out dataset that consists of 100 questions that are randomly selected from Bitext Gen AI Chatbot Customer Support Dataset³ .

We have created an imaginary company, TechStyle, which is an e-commerce website that has all its basic policies set. We also established a small customer database. These information files were provided to each AI agent vendor, after which we posed our questions. Our evaluation criteria consisted of the average of these three metrics:

• Factual accuracy: Does the response match TechStyle’s policies and customer data?
• Response completeness: Does it fully address what the customer asked?
• Professional helpfulness: Is it professional, empathetic, and actionable?

In the areas of factual accuracy and response completeness, we also recognized the vendors for protecting customer data rather than revealing it in chat responses. The evaluation of the responses was carried out by large language models assessing their similarity to the accurate answers provided by the Bitext dataset and our customizations for TechStyle’s data.

To illustrate, when the question “I expect a refund of $129.99 dollars, has it been processed?” was posed to the chatbots without any prior information about our identity (no customer login was made), the responses provided by:

• Azure: The refund of $129.99 for your Portable SSD Drive has been processed. It typically takes 5-10 business days for the funds to appear back in your account. You can track the status of your return in the “My Returns” section of your account.
• Tidio Lyro: You can check your refund status in the “My Returns” section of your account. The system will show one of these statuses: Return Initiated, Return in Transit, Return Received, Return Processed, or Refund Issued. You’ll also receive email updates at each stage.

For these reasons, we recommend you to:

• Choose Azure for comprehensive customer support in non-sensitive contexts. The baseline version of Azure does not protect data effectively and can expose anyone’s sensitive information. If the data you provide to Azure is not sensitive and only accessible to the public, it is a great tool. However, if your data is sensitive, you need to do a lot of fine-tuning.
• Choose Tidio for the best balance of helpfulness and security practices. Tidio’s approach of directing customers to secure account areas rather than exposing personal data in chat should be considered the gold standard for customer service AI.

Real-life AI agent use cases in customer service

1. Tidio’s Lyro

Gecko Hospitality, a recruitment services firm, employs Tidio’s Lyro AI agent alongside chat-flow automations to pre-qualify job applicants and handle routine inquiries constantly, 24/7. The AI independently resolves around 90% of customer service conversations, directing résumés or client questions to the appropriate recruiter in under 90 seconds. Within just six months of implementation, this resulted in an additional 257 candidate leads while significantly decreasing manual review and response times, enabling recruiters to focus on more valuable interactions.⁴

2. Ema’s Customer Support Agent

Envoy integrates Ema’s AI customer support agent for in-app assistance, saving 70%-80% of the support team’s time. This AI-powered solution streamlines customer service tasks and enhances efficiency.⁵

3. Bland.ai

Bland.ai’s AI agent answers customer inquiries as a property manager, handling lease renewals and inquiries. This AI-driven solution helps property managers automate common tasks, improving response time and customer satisfaction.⁶

4. Ada AI Agent

Wealthsimple utilizes the Ada AI agent to manage the workload of 10 full-time employees (FTEs). Ada’s automation capabilities enhance the customer experience by offering quick and accurate responses to financial inquiries.⁷

5. IBM Watson Assistant

Humana, a leading healthcare provider, deployed IBM Watson Assistant to manage healthcare-related inquiries. This AI solution reduced response times by 60%, improving customer satisfaction and operational efficiency.⁸

6. Beam AI’s Customer Service Agent

Avi Medical automates healthcare services with Beam AI’s customer service agent, cutting median response times by approximately 85%. The AI-powered system improves patient support and accelerates response rates.⁹

7. Sierra

WeightWatchers uses Sierra AI to achieve a 70% resolution rate in customer service interactions. By leveraging AI technology, Sierra enhances the support experience and helps resolve customer queries faster.¹⁰

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 2026.¹¹

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.

Further reading

• Compare 20 LLM Security Tools & Open-Source Frameworks 
• Enterprise AI agents
• AI agent builders
• Large action models (LAMs).

External Links

• 1. AI Agents for Customer Support Enhance AI Customer Service.
• 2. Salesforce’s Einstein Copilot is Here: The Conversational AI Assistant for CRM that Delivers Trusted AI Responses Grounded with Your Company Data - Salesforce. Salesforce
• 3. Bitext Gen AI Chatbot Customer Support Dataset | Kaggle.
• 4. Gecko Hospitality Reaches 90% Customer Service Automation. Tidio
• 5. How Envoy Global Leverages Ema for Enhanced Service and Efficiency - Ema.
• 6. Module 3 | Lesson 3: Case Studies and Best Practices.
• 7. Automate Troubleshooting and Resolve Inquiries with AI Customer Service | Ada.
• 8. Humana | IBM.
• 9. https://beam.ai/resources/case-studies/avi-medical
• 10. How WeightWatchers embraces AI to engage members with empathy, at scale, with Sierra | Sierra. Sierra
• 11. Gartner Predicts Conversational AI Will Reduce Contact Center Agent Labor Costs by $80 Billion in 2026.