8 Best AI Agent Platforms for Contact Centers

‍Information updated as of January 2026.

TL;DR: AI agent platforms help contact centers handle more conversations without adding headcount. Today's enterprise AI agents use multi-agent architectures with deterministic state management and layered guardrails to handle complex conversations reliably at scale. Pure automation platforms like Sierra and Decagon focus on replacing human agents entirely. Unified platforms like Cresta layer on top of existing infrastructure and bring AI agents, agent assist, and conversation intelligence together in one place, treating automation and human performance as connected rather than competing priorities.

Contact centers have more tools available to them than ever before. Better analytics, smarter routing, and AI-powered assistance have changed what teams can accomplish when they're handling thousands of customer conversations every day.

The most interesting development in this space is AI agent platforms. These aren't the clunky chatbots that frustrated customers a few years back. They are AI agents that can manage entire conversations on their own, figure out when to bring in a human, and hand off all the context so customers don't have to repeat themselves.

This article will walk you through the best AI agent platforms for contact centers and what to look for when evaluating them.

Best AI agent platforms for contact centers

1. Cresta

Cresta approaches AI agents differently from pure automation platforms. Rather than treating AI agents as a standalone product designed to replace human agents, Cresta built AI agents as part of a unified platform where automation and human performance reinforce each other. 

The same conversation data that trains AI agents also powers real-time guidance for human agents, and the same analytics that measure AI containment rates also track resolution quality and customer satisfaction across your entire operation. 

Beyond AI agents, Cresta's platform includes conversation intelligence that analyzes interactions across both automated and human conversations. Forrester recognized this capability when naming Cresta a Leader in The Forrester Wave for Conversation Intelligence Solutions for Contact Centers, Q2 2025, with the highest score in the Current Offering category.

Key capabilities:

• Conversation-grounded AI agents: Cresta AI Agent starts from real human conversations rather than scripts and SOPs. Automation Discovery identifies which interactions are suitable for automation based on complexity, frequency, and resolution patterns, grounding design in proven use cases.
• Sub-agent architecture: Specialized task-specific agents coordinated by a routing agent handle complex multi-intent conversations at scale. Deterministic state management tracks customer progress step by step, balancing LLM flexibility with predictable behavior.
• Voice performance: Low-latency response times backed by SLAs. Human-aware turn-taking distinguishes true interruptions from background noise and avoids talking over callers.
• Post-handoff continuity: When AI agents escalate, Cresta Agent Assist continues supporting human agents with real-time guidance and full handoff context. Customers do not repeat themselves, and teams maintain visibility after escalation.
• Agent Operations Center: Human-in-the-loop supervision lets supervisors monitor hundreds of simultaneous AI conversations and intervene when needed.
• Unified analytics: Conversation Intelligence analyzes all interactions across AI and human agents. Predictive CSAT scoring infers satisfaction without surveys. Organizations can benchmark both side by side.
• Enterprise guardrails: Four layers of defense: system-level, supervisory, adversarial testing, and automated behavioral QM. Certifications include SOC 2 Type II, HIPAA, PCI DSS, ISO 27001, and ISO 42001.

Who it's for: 

• Contact centers evaluating AI agents but unwilling to sacrifice visibility into what happens after escalation
• Teams that have tried point solutions and found themselves stitching together data from multiple systems
• Organizations in regulated industries where compliance monitoring needs to span both automated and human interactions without gaps

Visit the resource library to learn more or request a demo.

2. Sierra

Sierra is an autonomous AI agent platform that handles deployment through a fully managed service model. The company takes responsibility for coding, integrations, and implementation, letting organizations launch conversational agents without internal AI expertise.

Agent OS manages AI agents across voice, chat, and messaging with workflow setup, brand voice configuration, and policy enforcement. The fully managed service handles implementation complexity, integrations, and ongoing maintenance, so organizations can focus on defining what they want the AI to accomplish rather than building technical infrastructure.

Who it's for: Consumer brands prioritizing speed to deployment over internal control. Works well for organizations comfortable with vendor-managed implementations seeking autonomous agents for transactional customer interactions.

3. Decagon

Decagon is an autonomous AI agent platform for customer service that gives CX teams direct control over how AI agents behave without requiring engineering resources. The platform targets tech-forward companies that want hands-on management of their AI automation.

Agent Operating Procedures combine natural language instructions with code-like precision, letting CX operators build and modify AI agent logic without engineering support. Self-service customization allows teams to adjust escalation triggers, refine conversation flows, and modify agent behavior directly rather than waiting for vendor implementation cycles.

Who it's for: Tech-savvy CX teams, particularly in fintech and SaaS, that want direct control over AI agent behavior without engineering dependencies. The platform requires technical comfort within the CX organization, but rewards that investment with faster iteration and more granular control over automation logic.

4. Google Contact Center AI

Google Contact Center AI brings Google's natural language processing and machine learning capabilities to contact center automation. The platform integrates with Google Cloud infrastructure for organizations already invested in that ecosystem.

Dialogflow CX provides conversational AI with Google's natural language understanding, intent recognition, and entity extraction. The visual flow builder lets teams design conversation paths while leveraging Google's language models. Agent Assist delivers real-time suggestions to human agents during live conversations, surfacing relevant information and recommended responses based on conversation context.

Who it's for: Organizations already on Google Cloud who want AI capabilities integrated with existing infrastructure. Works well for technical teams comfortable leveraging Google's broader ecosystem and willing to invest in custom implementation.

5. Kore.ai

Kore.ai provides enterprise conversational AI with strength in no-code development and pre-built industry solutions. The platform offers templates and workflows designed for specific verticals, reducing time to deployment for common use cases.

The Experience Optimization Platform enables no-code conversational AI development with visual builders and pre-configured templates that business users can modify without engineering support. Industry-specific solutions for banking, healthcare, and retail include pre-configured workflows, compliance features, and domain terminology already built in.

Multi-channel deployment spans voice, chat, messaging, and email with unified conversation management across channels.

Who it's for: Organizations wanting pre-built industry solutions rather than building from scratch. Works well for banking, healthcare, and retail teams seeking faster deployment through templates and pre-configured workflows. The no-code approach trades some flexibility for accessibility and speed.

6. Cognigy

Cognigy provides enterprise conversational AI using a hybrid architecture that combines rule-based automation with large language model capabilities. The platform covers automation from self-service to agent assist, with particular strength in global deployments.

Agentic AI through the Nexus Engine combines large language model reasoning with real-time context, memory, and enterprise governance for more sophisticated conversation handling. Low-code and no-code AI Agent Studio lets business users and developers co-create AI agents with visual tools while maintaining technical depth for complex implementations.

The platform supports 100+ languages with a full automation spectrum, including conversational IVR, self-service, agent assist, and RPA.

Who it's for: Global enterprises needing multilingual support and flexible deployment options. The platform supports on-premise installations for organizations with specific data residency requirements and offers broad automation capabilities across voice and digital channels.

7. Forethought AI

Forethought AI is an agentic AI platform focused on measurable resolution rates rather than simple deflection metrics. The platform emphasizes end-to-end issue resolution with verification mechanisms ensuring accurate responses.

The product suite includes:

• Solve for autonomous resolution 
• Triage for intelligent routing
• Assist for agent guidance during human conversations
• Discover for identifying knowledge gaps and automation opportunities

The agentic AI approach emphasizes resolution verification, checking that responses actually address customer needs before delivery rather than optimizing purely for deflection. Forethought Voice extends AI capabilities to phone-based customer interactions, expanding automation beyond digital channels.

Who it's for: Support teams prioritizing resolution rates over containment metrics. Works well for organizations wanting verified, accurate AI responses and those expanding automation from chat to voice channels.

8. Retell AI

Retell AI provides developer-focused voice AI infrastructure with emphasis on low latency and natural conversation flow. The platform targets technical teams building custom voice agents rather than business users seeking turnkey solutions.

Voice AI infrastructure optimized for sub-second latency across the speech-to-response pipeline enables natural conversational rhythm without awkward pauses. Developer control through APIs and SDKs lets engineering teams build custom voice experiences with granular control over conversation logic, integrations, and behavior.

Emotion analysis detects customer sentiment during voice interactions, providing signals for escalation or conversation adjustment.

Who it's for: Technical teams with engineering resources building custom voice AI solutions. Works well for organizations with specific voice requirements that off-the-shelf platforms do not address, or those wanting to embed voice AI capabilities into proprietary applications. Requires development investment.

What are AI agents for contact centers?

AI agents are semi-autonomous systems that can often handle customer conversations end to end, while escalating more complex or high-risk cases to humans when needed. They use generative AI to understand what customers are asking for, take action to solve problems, and figure out when a conversation needs to be handed off to a person.

In contact centers, AI agents help solve a problem that's been getting worse for years. Call volumes keep growing while teams stay the same size, and agents end up spending a lot of their day on routine questions. AI agents can take over those predictable conversations around the clock, whether they come in by phone or chat, so human agents can spend their time on the issues that actually need their judgment. 

And because many modern platforms can be tuned over time using your outcomes and feedback, they get better based on the metrics your team actually cares about.

The big difference between AI agents and old-school chatbots is flexibility. Chatbots follow scripts, and they fall apart the moment a customer asks a follow-up question or changes topics. AI agents understand context and can adapt when conversations go in unexpected directions instead of forcing customers down a rigid path.

The architecture behind these systems has evolved significantly. Early chatbots used rigid decision trees. Single-agent LLM deployments followed, offering more flexibility but struggling with complex multi-step workflows. The current generation uses multi-agent architectures where a routing agent coordinates specialized sub-agents, each optimized for specific tasks like authentication, billing inquiries, or technical troubleshooting. 

Deterministic state management tracks each customer's progress step by step, ensuring the AI triggers appropriate actions at exactly the right moments.

What AI agents can do in contact centers

Today's AI agents do more than answer routine questions. They can handle full customer conversations from start to finish and know when to bring in a human with all the context intact.

AI agents manage routine conversations autonomously across voice and digital channels. They authenticate customers, look up account information, process transactions, and resolve common issues without human involvement. When conversations require human judgment, good AI agents recognize this early rather than frustrating customers with attempts to handle something beyond their capabilities.

What happens after escalation matters as much as the handoff itself. The best platforms pass full conversation history, extracted entities, actions already attempted, and suggested next steps to the human agent. This prevents customers from repeating themselves and gives agents complete context from the start. 

Some platforms continue supporting the human agent after handoff with real-time guidance, knowledge assistance, and automated summaries, creating continuity across the entire conversation rather than treating the AI and human portions as separate interactions.

AI agents also support multiple languages through real-time translation, letting teams help international customers without needing native speakers for every language.

What to look for in an AI agent platform

Evaluating AI agent platforms requires looking beyond feature lists to understand how vendors approach core technical and operational challenges. The following framework covers the capabilities that matter most for enterprise deployments.

• Agent architecture: Multi-agent architectures with routing agents coordinating specialized sub-agents handle complex conversations more reliably than single-agent approaches. Deterministic state management should track customer progress throughout conversations, ensuring the AI triggers appropriate actions at exactly the right moments.
• Guardrails and governance: Enterprise AI agents need layered defenses. System-level guardrails enforce non-negotiable rules, supervisory guardrails monitor behavior in real time, and AI-driven adversarial testing stress-tests agents against edge cases and malicious inputs.
• Testing and evaluation: Look for platforms that generate realistic test cases from historical conversations, use LLM-powered evaluators to check flow adherence and knowledge grounding, and provide regression protection to ensure improvements do not degrade performance elsewhere.
• Latency and conversation flow: Voice interactions expose latency problems immediately. Look for sub-second response times backed by SLAs, end-of-utterance detection, and human-aware turn-taking that distinguishes true interruptions from background noise.
• Human-agent collaboration and handoff: Effective platforms define clear boundaries between AI-handled and human-handled use cases, transfer full conversation context during handoffs, and maintain visibility after AI escalates to a human agent.
• Continuous improvement: Performance analytics should connect AI behavior to business outcomes. Root-cause analysis tools, A/B testing, and versioning with rollback capability help teams expand automation coverage safely over time.
• Platform and security: Unified platforms combining AI agents with agent assist and conversation intelligence offer compounding value over siloed tools. Security certifications, including SOC 2 Type II, ISO 27001, ISO 42001, HIPAA, GDPR, and PCI-DSS, should be non-negotiable.

Choosing the right AI agent platform for your organization

The evaluation framework above gives you the criteria. The main question to answer now is which criteria matter most for your situation.

Start with post-handoff visibility. If your AI agents will escalate even 10-15% of conversations to humans, what happens after that handoff affects your overall customer experience. Platforms that lose sight of conversations at escalation create blind spots in your analytics and force customers to repeat themselves.

Consider your technical resources honestly. Managed services get you to production faster, but put you on the vendor's timeline for customization. Developer-focused platforms offer more control but require engineering investment. Match the platform to the team you actually have.

Equally as important, think about what you are measuring. If containment rate is your primary metric, pure automation platforms optimize for exactly that. If you care about resolution quality, customer satisfaction, or revenue impact across both automated and human interactions, you need unified analytics that span both.

Cresta was built for the last scenario. The platform treats AI agents and human agents as connected problems, sharing the same data, analytics, and governance. AI agents trained on real conversation data perform better because they reflect how customers actually behave. 

Human agents picking up escalations get full context and continued real-time support. Leaders see performance across their entire operation in one place. And the platform layers onto your existing CCaaS infrastructure without a rip-and-replace. Request a demo to see how it works in your environment.

Frequently asked questions about AI agent platforms

Do AI agents replace human agents?

No. AI agents handle routine, predictable conversations so human agents can focus on complex issues that need judgment, empathy, or problem-solving skills. Most organizations use AI agents to scale capacity without proportionally growing headcount.

How is an AI agent different from an IVR?

IVR systems route calls using menu trees and touch-tone inputs. AI agents actually converse with customers, understand intent from natural language, and resolve issues on their own. When they cannot resolve an issue, they hand off to a human with full context instead of dropping the caller into a queue.

How long does it take to deploy an AI agent platform?

It varies. Specialized platforms that layer onto existing infrastructure can go live in weeks. Platforms requiring more custom implementation or integration work typically take longer, depending on complexity and internal resources.

Can AI agents handle sensitive industries like healthcare or finance?

Yes, but compliance matters. Look for platforms with SOC 2 Type II, documented HIPAA compliance, and PCI-DSS certifications where relevant. The platform should also support PII redaction and secure data handling. ISO 42001 certification for responsible AI provides additional assurance for enterprise deployments.