Gartner forecasts that by 2027, 25% of customer service operations will use conversational AI as a primary channel, up from less than 5% in 2024. Voice is the channel where that shift gets tested first. A caller who is upset about a frozen card, a missed appointment, or a benefit they cannot access does not want to navigate a menu. They want their problem solved on the call.

Most voice AI platforms still treat phone support as deflection. They answer FAQs, capture intent, then hand off to a human queue. That worked in 2023. It does not work for a head of CX who needs a voice agent to verify identity, pull data from a core banking system, file a claim, and update a ticket in Zendesk before the customer hangs up.

This comparison ranks 10 voice AI agents on workflow execution depth, weighted toward verifiable customer outcomes (containment rates, CSAT lift) over marketing claims.

What to look for in a voice AI agent that handles real workflows

Buying a voice AI agent in 2026 is not the same as buying a chatbot with a TTS layer on top. The hard part is not the voice. The hard part is what happens between the caller finishing a sentence and the agent producing a response: looking up the customer, checking eligibility, running a refund through your payments processor, writing a note back to your CRM, and deciding whether to escalate.

Buyers should evaluate voice AI agents on five dimensions:

• Workflow execution depth. Can the agent run more than one step in a single call? Can it call multiple tools in parallel? Can it coordinate sub-agents to engage third parties (a hotel, a card network, a payments processor) and report back on the same call?

• Cross-channel parity. Does the same workflow run on voice, chat, email, and SMS? If you train a refund workflow in chat, does it work the same way on a phone call, or do you have to rebuild it?

• Guardrails and audit trails. Are PII stripping, jailbreak detection, response grounding, and audit logs first-class features, or bolted on later? For regulated industries, this is non-negotiable.

• Latency under real-world load. Sub-1-second response is the floor. The question is whether it stays sub-second when the agent is making three tool calls and waiting on a slow backend API.

• Operator control. Can your CX ops team change a workflow without filing a ticket with the vendor? Or is every change a forward-deployed engineer engagement that takes two weeks?

Quick comparison table

How these were selected

We evaluated voice AI vendors against the following criteria, weighted toward operator-owned platforms with verifiable customer outcomes:

• Production deployments with named customers. Marketing site logos are not enough. We required at least one publicly verifiable case study or press mention showing the platform handling inbound or outbound voice in production.

• Multi-step workflow execution. The platform must do more than transcribe and respond. It must call backend tools, write data back to systems of record, and coordinate at least two sequential or parallel actions in a single call.

• Sub-1-second response latency. Any platform reporting end-to-end response times above 1.2 seconds was disqualified. Voice quality degrades fast above that threshold.

• Compliance posture. SOC 2 Type II is the floor. PCI DSS and HIPAA are required for finance and healthtech inclusions.

• Operator empowerment. Platforms that require forward-deployed engineering for every workflow change were scored lower than platforms that let operators self-serve.

We also weighted these evaluation factors:

• Cross-channel workflow parity (does the voice workflow share an engine with chat, email, SMS, and WhatsApp?)

• Audit trails and typed guardrails

• Pricing transparency and outcome-based pricing options

• Time from contract signed to first production call

• Quality of escalation handoff (transcript, context, intent passed to a human agent)

What is a voice AI agent?

A voice AI agent is software that handles a live phone conversation end to end: it picks up the call, understands what the caller wants, takes action against backend systems, and responds with natural speech. The modern stack combines real-time speech-to-text (STT), an LLM for reasoning, workflow logic for action execution, and text-to-speech (TTS) for the response.

The capabilities that separate a real voice agent from a fancy IVR replacement:

• Natural turn-taking with semantic voice activity detection and barge-in

• Parallel tool calling

• Sub-1-second response latency under load

• Multi-step workflow execution (verify, decide, act, write back, confirm)

• Live transfer to a human with full context

• Codeswitching between languages mid-call

• Voicemail detection on outbound

• Compliance controls (PII stripping, audit trails, response grounding)

The top 10 voice AI agents for customer support in 2026

1. Lorikeet

Best for: Regulated B2C and B2SMB businesses (fintech, healthtech, insurance, financial services) that need a voice agent to actually resolve complex calls, not deflect them.

Lorikeet is an AI concierge platform built for complex and regulated industries. Voice 2.0 launched in December 2025 and runs on the same workflow engine as chat, email, SMS, and WhatsApp. Operators train workflows once and deploy them across every channel with seamless handover. That cross-channel parity is rare. Most competitors either bolted voice onto a chat product (and the workflows do not match) or built voice-only and cannot do anything else.

The architecture that powers multi-step resolution is called Pockets of Determinism. Natural-language agents wrap structured sub-workflows that get called as tools, with validation inside each tool. This is what lets the voice agent run identity verification, dispute a transaction, send a confirmation SMS, and update a ticket in Zendesk without dropping context. The platform also supports parallel action execution: fraud-flag a card, coordinate with a third party, and process a declined payment simultaneously while keeping the call alive.

The voice stack is multi-vendor with failover at every layer: LiveKit for media and rooms, Twilio for SIP and PSTN, Deepgram Flux for STT (with keyword boosting), Cartesia Sonic-3 for TTS with ElevenLabs Turbo as backup, and GLM 4.7 as the primary LLM with Vertex AI failover. Synthetic uptime monitoring runs end-to-end test calls every two minutes, with per-vendor kill switches that flip when quality degrades.

Key features:

• Pockets of Determinism architecture for multi-step workflow execution

• Parallel action execution and Team of Agents (sub-agents that engage third parties via voice, text, Slack, or email and report back)

• Cross-channel workflow parity (voice, chat, email, SMS, WhatsApp on one engine)

• Typed guardrails (Alert, Steer, Escalate, Add Action) with PII stripping and audit trails

• Sub-1-second response latency; AGENTIC_LLM p50 around 395-470ms

• Native integrations with Twilio Flex, Amazon Connect, Salesforce Service Cloud Voice, Zendesk Talk, Intercom Phone, Genesys Cloud, and Dialpad

• Codeswitching between English and Spanish mid-sentence; Mandarin, Turkish, and French monolingual

• Conversation Lab simulation harness and Voice TQS scoring

Pricing: $1.50 per resolved voice call (Start tier), with lower per-resolution rates on Scale. Outcome-priced, not per-seat. You only pay for tickets the agent actually resolves.

Customer proof: GiveCard ran 60,000+ emergency calls in English, Spanish, and Mandarin during the 2025 SNAP shutdown, serving 300,000 cardholders with ~85% containment. Flex hit 94% CSAT in week 2 of deployment, with 2x CSAT versus their prior tool and 50% reduction in average call duration while handling a 4x volume surge. Berry Street runs 500-1,000 outbound appointment-reminder calls per day. Luxury Escapes handles 60-100 inbound calls per day on Genesys Cloud. Win rate against Sierra and Decagon in head-to-head evaluations: above 60%.

2. Sierra

Best for: Fortune 500 and Fortune 100 enterprises with the budget for forward-deployed engineering and a multi-quarter implementation timeline.

Sierra is the most-talked-about enterprise AI agent platform of the last two years, co-founded by Bret Taylor. It is a managed service: customers buy access to the platform plus a team of forward-deployed engineers who build the workflows. That model produces strong outputs at companies like SiriusXM, ADT, and Sonos. It also produces a slow deploy cycle, high entry-level pricing, and a dependency on Sierra engineers for every meaningful change.

Sierra's voice agent is genuinely capable and the platform is PCI DSS Level 1 certified, which makes it the right choice for some regulated workloads (specifically those involving in-call payment capture). The trade-off is operator empowerment. CX ops teams cannot self-serve workflow changes the way they can on Lorikeet or Decagon.

Key features:

• Strong enterprise governance and brand-tone alignment

• PCI DSS Level 1 certified (handles in-conversation payment capture)

• Voice + chat + SMS on a unified platform

• Forward-deployed engineering model for white-glove delivery

• Strong analytics and quality monitoring

Pricing: Custom enterprise pricing; typical ACV is reported in the $500K-$1M+ range.

3. Decagon

Best for: High-volume DTC, e-commerce, and SaaS companies that want a polished out-of-the-box AI concierge with less customization burden.

Decagon raised significant funding on the strength of its concierge framing and the polish of its product demos. The platform handles voice, chat, and email with a strong reporting layer and good prebuilt integrations. It works well for companies whose support volume is dominated by repeatable, lower-stakes interactions: order status, returns, basic account changes.

Where Decagon gets challenged is the complex and regulated end of the market. The platform is more batteries-included than Sierra or Lorikeet, which is a strength for simpler use cases and a constraint when you need to coordinate workflows across seven systems for a stressed customer. Buyers evaluating Decagon for fintech or insurance work should pressure-test guardrails and audit trails specifically.

Key features:

• Polished concierge UX with strong out-of-the-box behavior

• Voice, chat, and email channels

• Good prebuilt integrations with Zendesk, Intercom, and Shopify

• Reporting layer designed for ops teams

• Faster initial deploy than Sierra

Pricing: Custom; mid-six-figure ACV typical.

4. PolyAI

Best for: Multilingual contact center deployments where call containment and language coverage matter more than cross-channel workflow parity.

PolyAI is a voice-only specialist with deep telephony integration and very strong multilingual support. The platform regularly reports call containment rates above 80% for large enterprises (hotels, retailers, utilities). It plugs directly into existing contact center infrastructure (Genesys, Avaya, Cisco) without forcing a platform replacement.

The constraint is the voice-only scope. If you also need chat, email, SMS, and WhatsApp, PolyAI is not the answer. You would end up running two platforms with two workflow definitions and two reporting layers. For organizations that have settled on a separate digital-channels vendor and want a best-in-class voice layer, PolyAI is a strong choice.

Key features:

• Voice-only, contact-center-native

• Multilingual coverage across 12+ languages with high quality

• Call containment rates above 80% reported by customers

• Strong integrations with Genesys, Avaya, Cisco, and other CCaaS platforms

• SOC 2, PCI DSS, and GDPR compliant

Pricing: Custom enterprise; starting around $150K/year per deployment.

5. Cognigy

Best for: Enterprise contact center modernization projects where the buyer is replacing or augmenting an existing CCaaS deployment.

Cognigy is a German conversational AI platform with deep roots in enterprise CCaaS. It runs across voice, chat, and messaging channels with a flow-based builder that ops teams can use without engineering. The platform integrates with major contact center suites (Genesys, NICE, Avaya, Cisco). Cognigy is at its best when the buyer already has a mature contact center and wants to add a conversational AI layer on top. The flow-based heritage shows up in how the platform handles ambiguous intent and complex multi-step workflows.

Key features:

• Voice, chat, and messaging channels with a single flow builder

• Strong enterprise CCaaS integrations

• 100+ languages supported

• On-premise and private cloud deployment options

• Detailed analytics and reporting

Pricing: Custom enterprise quote.

6. Salesforce Service Cloud Voice (with Agentforce)

Best for: Companies already running Salesforce Service Cloud who want a voice layer that lives inside the same CRM record.

Salesforce Service Cloud Voice is the incumbent contact center option for Salesforce-native CX organizations. It brings inbound and outbound voice into the Service Cloud agent workspace, with full transcripts and real-time call insights. Agentforce, Salesforce's AI agent layer, adds AI-driven response and action capabilities on top. The platform is often the right choice for a Salesforce shop that wants to avoid the integration complexity of a third-party platform. Buyers should pressure-test Agentforce specifically on multi-step workflow execution.

Key features:

• Native Salesforce Service Cloud integration

• Inbound and outbound voice in the same agent workspace

• Agentforce AI agents for response and action

• Strong analytics inside the Salesforce reporting layer

• Familiar admin experience for existing Salesforce teams

Pricing: $75-$165 per user/month for Service Cloud Voice; Agentforce pricing is consumption-based.

7. Retell

Best for: Builders who want to construct a voice agent on top of a managed infrastructure layer.

Retell is a voice infrastructure platform that handles the hardest parts of the real-time voice loop: STT, TTS, turn-taking, telephony, and call orchestration. You bring the agent logic (LLM, workflow, integrations). The result is fast, transparent per-minute pricing and a lot of control, in exchange for more engineering work on your side.

Retell is in a different category than Lorikeet, Sierra, or Decagon. Those are full agent platforms. Retell is the layer underneath them. If you have engineering capacity and a strong opinion on how your workflows should run, Retell can be the right choice. If you are a head of CX without an engineering team, Retell is not.

Key features:

• Sub-second latency with strong barge-in and turn-taking

• Transparent per-minute pricing

• Twilio, SIP, Salesforce, and HubSpot integrations

• SOC 2, HIPAA, and GDPR compliant

• BYO LLM and TTS

Pricing: Usage-based, starting around $0.07 per connected minute, plus LLM and TTS pass-through.

8. Vapi

Best for: Developer teams building voice agents from the ground up with full control over the stack.

Vapi is a voice agent platform aimed at developers. It handles STT, TTS, telephony, and orchestration with a developer-first API. You bring the agent logic, integrations, and workflow design. Vapi is positioned similarly to Retell; the choice between them comes down to developer experience preferences and minor differences in concurrency, latency, and supported vendors. Neither is the right pick for a CX leader looking for a managed AI concierge.

Key features:

• Developer-first API for voice agent orchestration

• BYO LLM and TTS

• Twilio and SIP telephony integrations

• Sub-second response latency

• Open-source SDK and community

Pricing: Approximately $0.05 per minute plus LLM and TTS pass-through.

9. Bland

Best for: Outbound voice campaigns that need to scale to massive concurrency with strict governance controls.

Bland is a voice AI platform focused on extreme scale and security. The platform claims support for up to one million concurrent calls, which is overkill for most inbound support deployments and useful for high-volume outbound campaigns (debt collection, appointment reminders, sales outreach). For inbound customer support where multi-step workflow execution matters, Bland is less compelling. It is built around the assumption that you have your own workflow logic and want a hardened voice engine to run it on.

Key features:

• Extreme concurrency (claimed up to 1M concurrent calls)

• Strong governance and policy controls

• API-first architecture

• Realistic voice synthesis

• GDPR-friendly

Pricing: Usage-based, no public pricing.

10. Yellow.ai

Best for: Multi-region enterprise support operations that need a broad channel footprint with regional language coverage.

Yellow.ai is an enterprise conversational AI platform with strong coverage across voice, chat, messaging, and email. Its largest deployments are in Asia-Pacific and the Middle East, where it has built deep language coverage for regional dialects. The trade-off, as with Cognigy, is workflow depth versus channel breadth. Yellow.ai's flow builder is mature and well-suited to standard support workflows. For the complex, regulated, multi-step workflows that the rest of this list optimizes for, more specialized AI concierge platforms typically outperform.

Key features:

• Voice, chat, messaging, and email on one platform

• 135+ languages supported

• Strong enterprise integrations across CRMs and helpdesks

• Flow-based builder with templates

• Detailed analytics

Pricing: Custom enterprise quote.

How to choose the right voice AI agent for your business

The right voice AI agent depends on the complexity of your workflows, your industry's regulatory posture, and how much engineering capacity sits behind your CX function. Five evaluation criteria separate good fits from expensive misfires.

1. Map your most common multi-step calls before you evaluate. Pick the five highest-volume call types in your contact center. For each, write down every step a human agent takes today: the systems they open, the data they look up, the decisions they make, the actions they execute. If you cannot describe the workflow on paper, no voice AI platform will execute it well. This map is also what you will use to score vendor demos. Make every vendor walk through your real workflows, not their canned demo scripts.

2. Demand a live demo on your data inside two weeks. Vendors that take six weeks to stand up a proof of concept are showing you their delivery model, not their tech. Modern AI concierge platforms can build a working POC against your real systems in 7-14 days. If a vendor cannot deliver that, you are buying their forward-deployed engineering team, not their product. Decide whether that is what you want.

3. Pressure-test guardrails specifically. For regulated industries, the difference between a strong voice agent and a liability is what the agent does when something goes wrong. Ask every vendor: what happens if a caller asks the agent to bypass an identity verification step? What happens if the LLM produces a response that contradicts your policy? Look for typed guardrails (alert, steer, escalate, add action) with auditable trails. A vague claim that the platform has guardrails is not a real answer.

4. Verify cross-channel parity if you have it as a requirement. If you already run chat and email AI and you are adding voice, the worst outcome is running three separate workflow engines. Ask the vendor whether the voice workflow you build will run unchanged on chat, email, and SMS. The honest answer for most vendors is partial parity. A few platforms have full parity. Verify which group your vendor is in.

5. Get pricing aligned with outcomes, not seats. Per-seat licensing made sense when you had human agents on the seats. Voice AI agents are not seats. The honest pricing model is per resolved interaction. If a vendor will not give you outcome-based pricing, that usually means they are not confident in the outcome rate. Ask for a per-resolution price; if they refuse, ask why.

6. Run a 30-day post-launch quality review before you commit. Most voice AI quality problems do not show up in the POC. They show up at week three, when call volume goes up, edge cases surface, and the workflows you did not test get tested by real callers. Build a 30-day quality review into the contract. Define what success looks like. Reserve the right to walk if the platform misses the targets.

Detailed feature matrix

Why Lorikeet wins on multi-step workflows

The dimension that separates Lorikeet from the rest of the field is workflow execution depth. Three architectural choices compound to make this work.

The first is Pockets of Determinism. Most LLM-based voice agents run a single prompt loop and hope the model picks the right tool. That falls apart on complex regulated work where you need to chain identity verification, eligibility checks, and action execution in a specific order. Lorikeet wraps natural-language agents around structured sub-workflows that get called as tools, with validation inside each tool. The agent reasons in natural language. The actions run deterministically.

The second is parallel action execution. When a caller asks to dispute a transaction during a trip, the agent can fraud-flag the card, coordinate with the hotel, and process the declined payment simultaneously while keeping the call alive and the caller updated. Most platforms still run actions sequentially, which means a multi-step workflow stretches the call to four or five minutes. Lorikeet customers see workflow time cut in half.

The third is cross-channel parity. The same workflow engine runs voice, chat, email, SMS, and WhatsApp. Operators train once and deploy across. Only Sierra matches this breadth, and Sierra is a managed service while Lorikeet is operator-owned. The combination produces results buyers can verify:

• GiveCard served 300,000 cardholders during the 2025 SNAP shutdown, running 60,000+ emergency calls in English, Spanish, and Mandarin with ~85% containment. Peak day: 9,000+ tickets handled.

• Flex hit 94% CSAT in week 2 of deployment, with 2x CSAT versus their prior tool and 50% reduction in average call duration while handling a 4x volume surge.

• Berry Street runs 500-1,000 outbound appointment-reminder calls per day; 800 calls in a single day on January 22, 2026.

• Luxury Escapes handles 60-100 inbound calls per day on Genesys Cloud.

• Joy Parenting went from kickoff to production launch in 7 days.

• Head-to-head win rate against Sierra and Decagon: above 60%.

During a difficult and stressful situation, we could rely on Lorikeet voice agents to guide cardholders through the process of accessing their benefits with clarity and care.

- Sofia Pedro, Head of Product, GiveCard

Lorikeet is honest about where it does not win yet. PCI DSS Level 1 is in progress, not certified (Sierra is the right pick if in-call payment capture is required). English is the strongest language; Spanish, Mandarin, Turkish, and French work but accents and codeswitching can degrade quality. Persistent customer memory across sessions is on the Q2 2026 roadmap. Buyers who need any of those today should weight that in the evaluation.