Best AI Agents for Customer Support Automation in June 2025

In an era defined by immediacy and personalization, customer service has become one of the most critical touchpoints in shaping a company’s reputation. As businesses scale their operations and serve increasingly diverse and global audiences, traditional customer support frameworks—reliant on static knowledge bases and limited human bandwidth—are proving insufficient. This shift in expectations has paved the way for a new generation of solutions: AI-powered customer support agents.

By June 2025, AI agents are no longer experimental add-ons. They are rapidly becoming core infrastructure within customer service departments, driving efficiency, improving satisfaction, and reducing operational costs. Leading enterprises such as Salesforce and Verizon have reported remarkable advancements in automation. Salesforce’s “Agent Force” is now capable of autonomously handling between 30% to 50% of routine service tasks, a development its CEO lauded as emblematic of AI’s growing dominance. Meanwhile, Verizon has deployed a Gemini-powered AI assistant to support millions of customers with billing, plan upgrades, and device troubleshooting—streamlining resolution times and relieving pressure on human teams.

Simultaneously, the emergence of specialized startups like Synthflow AI is reshaping the real-time voice support landscape. With ultra-low-latency agents capable of responding in under 400 milliseconds, these platforms are proving invaluable across high-stakes industries such as finance, healthcare, and logistics. Their ability to rapidly scale, integrate with existing telephony infrastructure, and offer multilingual capabilities has significantly narrowed the gap between automation and human service quality.

Behind these innovations are the advances in large language models (LLMs) and multi-agent orchestration. From GPT-4 and Google Gemini to proprietary enterprise engines, modern AI agents are capable of maintaining contextual awareness, responding empathetically, escalating appropriately, and even learning from each interaction to improve future performance. More importantly, they are increasingly adaptable—available as chatbots, voice assistants, and hybrid deployments across live support channels.

This blog offers a comprehensive overview of the best AI agents available for customer support automation as of June 2025. From industry veterans like Zendesk and Intercom to cutting-edge entrants like Kruti and Synthflow, we will assess each platform’s capabilities, core features, ideal use cases, and market momentum. We will also explore how to evaluate these tools based on your business needs, followed by key trends reshaping the future of support automation.

Whether you’re leading a customer success team or evaluating your company’s digital transformation strategy, understanding the landscape of AI agents is no longer optional—it’s essential.

The integration of artificial intelligence into customer service is no longer a theoretical proposition—it is a pragmatic solution to some of the most pressing challenges faced by modern enterprises. As consumer expectations for round-the-clock, personalized, and rapid support grow, organizations are increasingly turning to AI-powered customer support agents to meet demand with precision, scale, and consistency.

From Static Chatbots to Dynamic AI Agents

The initial wave of customer service automation was characterized by rule-based chatbots, programmed to deliver scripted responses to a narrow set of questions. While useful in addressing repetitive inquiries, these early systems lacked context-awareness, failed in complex scenarios, and often frustrated customers with rigid flows.

Fast forward to 2025, and the landscape has dramatically evolved. Today’s AI agents are intelligent, adaptive, and capable of contextual understanding thanks to breakthroughs in natural language processing (NLP) and large language models (LLMs). Tools built on architectures like GPT-4, Gemini, and Anthropic’s Claude can now analyze queries, infer intent, retrieve relevant information, and deliver responses that closely resemble human interaction. The result is a shift from reactive bots to proactive, conversational agents that can initiate dialogue, resolve ambiguity, and escalate issues intelligently when needed.

Why Enterprises Are Prioritizing AI Agents

There are several strategic and operational reasons why customer service departments are prioritizing AI agents:

1. Scalability: Human agents, regardless of their efficiency, cannot scale infinitely. AI agents, however, can handle thousands of concurrent interactions without fatigue or decline in quality.
2. 24/7 Availability: Customers increasingly expect support across all time zones. AI agents provide always-on availability, reducing the need for overnight shifts and global support teams.
3. Faster Resolution Times: By instantly retrieving data, navigating systems, and auto-resolving routine queries, AI agents reduce average handling time (AHT) and improve first-contact resolution rates.
4. Cost Efficiency: Companies report savings of up to 40% in operational support costs after implementing AI agents, especially in high-volume support channels.
5. Consistency and Compliance: AI agents offer uniform communication standards, minimizing human errors, bias, or variability—especially important in regulated industries like healthcare, banking, and insurance.
6. Data Insights: Every interaction with an AI agent is a data point. Advanced platforms now offer built-in analytics, customer sentiment tracking, and behavior-based escalation models to optimize CX workflows continuously.

A Surge in Adoption Across Industries

Customer service AI adoption has risen dramatically in the past two years. According to IDC, more than 63% of global enterprises have implemented AI agents for at least one part of their support operations as of Q2 2025. In sectors such as telecommunications, retail, banking, and SaaS, adoption rates are approaching saturation.

Companies like Salesforce, Verizon, and Amazon now rely on multi-agent AI systems—combinations of chat, voice, and hybrid agents that operate in tandem with human teams. Notably, Salesforce’s Agent Force is now capable of autonomously addressing a substantial volume of daily support tickets, while Verizon has reported a significant increase in customer satisfaction (CSAT) following the deployment of its Gemini-powered voice assistant.

Emerging players are also gaining traction. Synthflow AI, for instance, has gained popularity for its real-time voice agents with sub-400 millisecond response latency. Its platform supports over 20 languages and integrates directly with legacy call centers and modern cloud-based communication systems—making it a versatile choice for enterprises with diverse infrastructure.

The Role of LLMs and Agentic Frameworks

At the core of this shift is the agentic paradigm—the concept that AI systems are not just tools but semi-autonomous collaborators. Modern AI agents can:

• Recall previous user context.
• Access internal knowledge bases and CRM data.
• Operate across modalities (text, voice, visual).
• Self-correct based on user feedback.

These traits enable the development of multi-turn, memory-enabled agents that learn and improve over time, moving beyond scripted playbooks to deliver personalized and evolving customer experiences.

As the adoption of AI agents becomes a strategic imperative across industries, the market for intelligent customer service platforms is expanding rapidly. From large enterprises to agile startups, a wide array of vendors are offering AI-powered agents designed to automate support functions while enhancing user experience. This section profiles the 10 most impactful AI agents as of June 2025, highlighting their capabilities, use cases, and differentiating features.

1. Salesforce Agent Force

Overview:
Salesforce Agent Force is a robust enterprise-grade AI agent built into the broader Salesforce Service Cloud platform. It operates as a conversational layer across Salesforce’s ecosystem, enabling intelligent case resolution, knowledge surfacing, and workflow execution.

Key Features:

• Deep CRM integration with Salesforce data.
• AI-driven routing, case summarization, and escalation logic.
• Natural language processing (NLP) powered by proprietary LLM fine-tuning.

Best For:
Large enterprises needing tightly coupled CRM and AI support.

2. Verizon Assistant (Powered by Google Gemini)

Overview:
Launched in collaboration with Google, Verizon’s Gemini-powered AI agent handles millions of customer interactions across billing, technical support, and device management.

Key Features:

• Based on Google’s Gemini model, ensuring high-quality multilingual support.
• Escalation pathways to human agents with full transcript handoff.
• Integrated with Verizon’s app, website, and IVR systems.

Best For:
Telecom providers and large-scale B2C operations requiring voice + chat parity.

3. Synthflow AI

Overview:
Synthflow AI specializes in real-time voice agents, setting itself apart with ultra-fast response times and flexible deployment. The Berlin-based company raised $20 million in early 2025 to expand its global presence.

Key Features:

• Voice latency under 400ms for natural conversations.
• Pre-built templates for finance, healthcare, and education.
• No-code interface and compatibility with legacy phone systems.

Best For:
Businesses seeking real-time voice automation with rapid deployment.

4. Zendesk AI (Answer Bot & AI Copilot)

Overview:
Zendesk has evolved its AI suite with Answer Bot and the newer AI Copilot, combining knowledge-base automation with dynamic customer support guidance.

Key Features:

• Integrated seamlessly with Zendesk’s ticketing system.
• AI Copilot supports human agents with live suggestions.
• Multiple language support and channel flexibility.

Best For:
Organizations already using Zendesk or scaling their ticket volume.

5. Intercom Fin

Overview:
Intercom’s Fin AI assistant is powered by OpenAI and fine-tuned for support use cases. It provides conversational experiences via Intercom Messenger and integrates with Intercom Help Center.

Key Features:

• Built on GPT-4 with Intercom-specific optimization.
• Offers automatic fallback to human support if Fin can’t answer.
• Branded, customizable UI for customer touchpoints.

Best For:
Startups and mid-sized SaaS companies focused on customer experience.

6. HubSpot Breeze (Service Hub AI)

Overview:
HubSpot’s Breeze is part of its Service Hub AI suite. It targets small and medium businesses seeking lightweight, efficient AI integrations within their CRM.

Key Features:

• AI ticket triage, auto-responses, and chatbot builder.
• Deep HubSpot CRM integration and analytics.
• Includes intent recognition and predictive support modeling.

Best For:
SMBs already using HubSpot’s sales and marketing tools.

7. Gupshup

Overview:
Gupshup is a powerful conversational AI platform known for its flexible workflows, omnichannel capabilities, and agentic AI architecture. It has become a top choice for global messaging-based support.

Key Features:

• Prebuilt templates for WhatsApp, Messenger, Telegram, and SMS.
• Agent handoff, lead generation, and commerce integration.
• Multiple language and dialect support for global deployment.

Best For:
Enterprises focusing on mobile-first customer interaction.

8. Amelia (by IPsoft)

Overview:
Amelia, developed by IPsoft, is a well-established AI agent platform deployed across Fortune 500 companies. Known for its cognitive architecture, Amelia supports complex decision-making and live agent augmentation.

Key Features:

• Human-like interactions with contextual memory.
• Operates across voice, chat, and email.
• Integrates with ERP and enterprise backends.

Best For:
Highly regulated industries like healthcare, finance, and insurance.

9. Conversica

Overview:
Conversica focuses on revenue-generating AI assistants, particularly for lead engagement, retention, and follow-ups across sales and support.

Key Features:

• Multichannel outbound conversations (email, SMS, chat).
• Focused on customer lifecycle automation.
• Real-time alerts to sales and CS teams.

Best For:
Businesses aligning support and sales enablement functions.

10. Kruti (by Ola Krutrim)

Overview:
Launched in June 2025, Kruti is an Indian-built AI agent specializing in multilingual and culturally adapted customer service automation. Built on the Krutrim large language model, it supports 13 Indian languages out of the box.

Key Features:

• Regional voice and language adaptation.
• Low-resource deployment for local businesses.
• Optimized for mobile-first and tier-2/3 city access.

Best For:
Businesses targeting emerging markets in India and Southeast Asia.

With a growing number of AI platforms offering advanced customer service capabilities, selecting the right AI agent for your business requires more than just identifying trending tools. It involves aligning your customer experience (CX) objectives with the specific capabilities, deployment models, and operational frameworks of each platform. In this section, we outline the key considerations and decision-making criteria for evaluating AI customer support agents in 2025.

Define Your Primary Support Channels

The first and most fundamental step is to identify which communication channels matter most to your business. AI agents are no longer confined to just live chat. Today’s solutions span:

• Text-based interfaces (web chat, email, SMS)
• Voice-based systems (IVR replacements, real-time assistants)
• Multimodal deployments (e.g., chat with document recognition)

If your support strategy heavily involves phone-based interactions, platforms like Synthflow AI, Verizon Assistant, and Amelia are optimized for low-latency, natural voice support. Conversely, companies with chat-heavy workflows may find greater value in Intercom Fin, Zendesk AI, or HubSpot Breeze.

Match Agent Capabilities to Use Case Complexity

Not all customer queries are equal in complexity. A retail store answering FAQs about returns requires a very different AI system than a healthcare provider managing appointment scheduling or claims processing.

Here’s a practical framework for alignment:

• Low complexity (FAQs, order tracking): Use lightweight bots with prebuilt templates (e.g., Gupshup, HubSpot Breeze).
• Medium complexity (CRM queries, billing issues): Opt for agents with contextual memory and CRM integration (e.g., Salesforce Agent Force, Zendesk AI).
• High complexity (regulatory support, voice diagnostics): Deploy cognitive agents with dynamic escalation logic and advanced reasoning (e.g., Amelia, Synthflow AI).

Consider CRM and Ecosystem Integration

A powerful AI agent should not operate in isolation. Seamless integration with your CRM, help desk, analytics tools, and customer databases is crucial for enabling full-service automation and personalized responses.

• If your company uses Salesforce, its native Agent Force provides the best plug-and-play solution with full backend synchronization.
• For those on HubSpot, the built-in Breeze agent allows easy onboarding and unified ticket resolution.
• Intercom users benefit from Fin’s GPT-4 intelligence, backed by Intercom’s Help Center content, without the need for third-party connectors.

Integration compatibility should also be examined for legacy systems—especially in sectors like banking, telecom, and government. Agents like Amelia and Synthflow AI offer connectors for ERP, SIP, and traditional contact center solutions.

Evaluate Deployment Time, Cost, and Scalability

Another critical factor is how quickly you can deploy, how much it costs, and how well it scales with your growth. AI agent solutions vary widely:

• No-code and SaaS options (e.g., Synthflow AI, Gupshup, HubSpot Breeze) can often be launched within hours or days.
• Enterprise-grade platforms (e.g., Salesforce, Amelia) may require several weeks for integration, training, and security audits.

Cost structures typically fall into the following models:

• Monthly per-agent subscription (e.g., Intercom Fin, Zendesk AI)
• Usage-based pricing (e.g., Gupshup’s pay-per-interaction model)
• Enterprise license tiers (e.g., Salesforce Agent Force)

Your choice should reflect your budget, team size, expected conversation volume, and future scaling needs.

Regional and Language Considerations

In 2025, multilingual capability is not optional—it’s strategic. Businesses serving diverse geographies must ensure their AI agents can:

• Understand and respond in regional dialects
• Maintain tone consistency across languages
• Escalate or localize issues for compliance or cultural nuance

Kruti, launched by Ola Krutrim, has emerged as a standout in this domain, supporting 13 Indian languages with culturally adapted dialogue models. Google Gemini, as seen in Verizon Assistant, also excels in real-time multilingual understanding.

If your business spans multiple regions, prioritize agents with broad language support and natural localization frameworks.

Compliance, Data Privacy, and Security

As AI agents handle more sensitive data, security and compliance become essential. Enterprises should look for:

• End-to-end encryption for all customer interactions
• Audit logging and access control
• Adherence to local regulations (e.g., GDPR, HIPAA, India’s DPDP Act)

Platforms like Amelia and Salesforce have invested heavily in compliance, making them preferred options for regulated industries.

Final Thoughts

Choosing the right AI customer support agent is not about selecting the most powerful or expensive platform—it’s about selecting the most suitable and sustainable solution for your specific needs. By clearly defining your use case, understanding deployment capabilities, and evaluating performance metrics, you can confidently invest in a tool that not only streamlines operations but also enhances customer satisfaction and loyalty.

As businesses race to optimize customer interactions, AI is no longer merely augmenting service—it’s reinventing the model. The first half of 2025 has seen breakthrough developments that are not just reshaping how AI agents operate but also expanding their role within broader enterprise ecosystems. From agentic AI to real-time multilingual voice assistants, the current trends point to a future where AI is both the first and preferred point of contact for most customers.

Agentic AI: The Autonomous Support Layer

One of the most defining developments of 2025 is the emergence of agentic AI—intelligent systems that act semi-autonomously, make decisions based on goals, and coordinate tasks across multiple systems. Unlike scripted chatbots that follow linear flows, agentic AI agents can:

• Infer user intent across multiple steps.
• Access APIs, perform backend tasks (e.g., update an order, trigger a refund).
• Learn and improve over time without manual reprogramming.

Salesforce’s Agent Force and Zendesk AI Copilot now embody this paradigm, functioning as dynamic collaborators that augment both customer and employee workflows. These agents are increasingly able to initiate actions proactively rather than reactively, marking a major shift in enterprise support architecture.

Voice is Back—and Smarter Than Ever

While chat-first interfaces dominated early AI deployments, voice-based interaction is experiencing a powerful resurgence in 2025. Startups like Synthflow AI and corporate deployments like Verizon’s Gemini Assistant demonstrate how ultra-low-latency, LLM-powered voice agents can handle complex queries with natural cadence, memory retention, and emotion detection.

Real-time voice AI now enables:

• Sub-second response latency.
• Multilingual understanding in diverse dialects.
• Emotional intelligence (sentiment recognition, stress detection).

This is transforming contact centers, IVRs, and voice-first experiences in industries ranging from telecom to logistics to healthcare. Voice is no longer a fallback—it is becoming a strategic entry point.

Multilingual and Hyper-Localized Support

In a global market, AI agents must accommodate linguistic and cultural diversity. The trend toward hyper-localized AI is accelerating, with platforms like Kruti leading the charge in India. These agents go beyond translation—they understand:

• Regional expressions and idioms.
• Cultural expectations for tone and pacing.
• Local regulations that shape how queries must be answered.

Google’s Gemini and Anthropic’s Claude have been particularly successful in zero-shot and few-shot multilingual comprehension, making them effective backbones for global support deployments.

Human-AI Collaboration via Copilots

AI agents are no longer just front-line responders—they are becoming internal copilots for human agents. This dual-agent model enables:

• AI to surface relevant help articles or past tickets in real time.
• Live summarization of chat histories for agents joining ongoing interactions.
• Onboarding support for new agents via real-time guidance.

Zendesk AI Copilot, Intercom Fin Assist, and HubSpot’s Smart Compose are examples of copilots that reduce the cognitive burden on human agents while ensuring continuity and accuracy in customer support.

No-Code, Low-Code Democratization

As demand for support automation grows across sectors, so does the need for accessible AI development platforms. No-code and low-code environments are democratizing AI deployment by allowing customer experience managers to:

• Build and update workflows without engineering teams.
• Customize responses using drag-and-drop logic.
• Monitor performance with built-in dashboards.

Platforms like Synthflow, Gupshup, and HubSpot Breeze exemplify this shift, enabling even SMBs to design powerful agents with minimal investment.

Ethics, Privacy, and Regulation: The New Frontier

With great power comes the need for great governance. As AI agents become central to handling sensitive data—from personal identifiers to financial information—data privacy and compliance are emerging as boardroom-level priorities. Key regulatory trends include:

• Data sovereignty rules impacting cross-border AI deployment.
• Real-time audit trails required by financial services regulators.
• AI transparency disclosures being adopted across jurisdictions.

Platforms like Amelia and Salesforce offer compliance-grade infrastructure, including end-to-end encryption, audit logs, and redaction controls, setting the benchmark for responsible AI.

As we reach the midpoint of 2025, it is evident that AI customer support agents have transcended novelty and become essential components of the modern service stack. What began as a convenience feature—automated replies to basic inquiries—has rapidly matured into a robust infrastructure capable of resolving complex issues, enhancing user satisfaction, and operating continuously at scale.

The rise of agentic AI, the resurgence of real-time voice automation, and the democratization of low-code deployment are not isolated innovations—they are part of a systemic evolution in how businesses engage with their customers. From Salesforce’s Agent Force automating half of all support tasks, to Verizon’s Gemini-powered voice assistant, to Synthflow AI’s sub-second multilingual voice responses, leading organizations are proving that intelligent agents can outperform legacy systems in both speed and service quality.

Furthermore, the increasingly global and multilingual nature of modern commerce demands support solutions that are adaptable, culturally sensitive, and secure. Platforms like Kruti and Gupshup are pioneering this new frontier, offering regionally tailored solutions that lower barriers to digital engagement in emerging markets.

However, selecting the right AI agent is not merely about adopting the most advanced technology—it is about making a strategic, operational, and ethical choice. The best platforms not only reduce costs and response times but also empower support teams, enhance customer trust, and provide actionable insights into user behavior.

Looking ahead, the trajectory is clear: customer service organizations that invest early and strategically in AI agents will be better equipped to deliver consistent, scalable, and personalized experiences in an increasingly competitive landscape. Those that hesitate risk falling behind as automation becomes the standard, not the exception.

Whether you are a startup scaling fast or an enterprise undergoing digital transformation, the time to evaluate and implement AI customer service agents is now.