Insights & Use Cases
January 6, 2026

6 best orchestration tools to build AI voice agents in 2026

Build better AI voice agents with the right orchestration tool. Compare platforms, features, integrations, and real-world performance.

Jesse Sumrak
, 
Featured writer
AI voice agents
Conversation AI
Streaming Speech-to-Text
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AI voice agents, which a recent survey finds 62% of organizations are now experimenting with, turn frustrating IVR trees into actual conversations that get things done. These systems understand natural speech, maintain context throughout interactions, and respond with voices that sound (sometimes indistinguishably) human.

Behind most great voice agents is an orchestration tool that connects the necessary models—speech-to-text (STT) that captures what customers say, large language models (LLMs) that understand intent, and text-to-speech (TTS) that delivers natural responses. When these pieces work in harmony, customers get the help they need without the friction.

This guide covers what AI voice agents are, how they work technically, and the orchestration tools you need to build them successfully. You'll learn about the business value they deliver, implementation approaches, and how to choose the right platform for your specific use case.

What are AI voice agents

AI voice agents are conversational AI systems that understand and respond to human speech in real time using a stack of AI models. They handle complex, multi-turn conversations and adapt to natural language, unlike rigid IVR systems that force callers through predefined menu paths.

Think of it as the difference between following a strict flowchart and having an actual conversation. While a traditional IVR asks you to "press one for sales," a voice agent lets you say, "I need to check the status of my recent order and also ask about your return policy." The agent understands both intents and can switch context seamlessly.

Voice vs. Text Interface: Voice agents process spoken language, while chatbots handle text. This creates unique challenges around accent recognition, background noise, and interruption handling.

Key Distinction: Voice agents maintain conversational context, handle interruptions, and execute complex tasks while sounding increasingly human.

How AI voice agents work

AI voice agents operate on a three-part pipeline that executes in milliseconds. This orchestration separates them from simpler voice commands.

The Voice AI pipeline

Component

Function

Key Requirement

Speech-to-Text (STT)

Converts speech to text

Ultra-low latency

Large Language Model (LLM)

Understands intent, generates responses

Context awareness

Text-to-Speech (TTS)

Converts text to natural audio

Human-like quality

Speech-to-text (STT): Transcribes spoken words with high accuracy and extremely low latency. The model must detect when speakers finish thoughts through endpointing.

Large Language Model (LLM): Acts as the agent's brain, determining intent and generating appropriate responses. Can call external tools or APIs when needed.

Text-to-speech (TTS): Converts responses into natural-sounding audio. Voice quality and speed are critical for maintaining human-like interactions.

Additional components for production systems

Beyond the core pipeline, production voice agents require additional capabilities:

  • Turn-taking models: Determine when the user has finished speaking and when it's appropriate for the agent to respond
  • Interruption handling: Allows users to cut in mid-response without breaking the conversation flow
  • Context management: Maintains conversation history across multiple turns and remembers key information
  • Tool calling: Connects to external APIs and databases to fetch information or perform actions
  • Error recovery: Gracefully handles misunderstandings and guides conversations back on track
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Use cases and applications for AI voice agents

Developers are building AI voice agents across industries for diverse use cases. Each application leverages the core technology differently.

Customer Service:

24/7 technical support, order management, account services

Sales Operations:

Lead qualification, appointment setting, product recommendations

Healthcare:

Appointment scheduling, medication adherence, pre-visit screening

Internal Operations:

Data collection, field service support, employee services

Customer service and support

  • 24/7 Technical Support: Voice agents walk users through troubleshooting steps at any time of day, freeing up human experts for more complex problems
  • Order Management: Handle order status checks, modifications, and cancellations without human intervention
  • Account Services: Process password resets, billing inquiries, and account updates securely through voice verification

Sales and revenue operations

  • Inbound Lead Qualification: Agents can answer initial calls, ask qualifying questions, and route high-intent leads directly to sales teams
  • Outbound Appointment Setting: Companies use voice agents to call leads, find convenient times, and book appointments directly on calendars
  • Product Recommendations: Guide customers through product selection based on their needs and preferences

Healthcare coordination

  • Appointment Scheduling: Medical practices use voice agents to manage booking, rescheduling, and appointment reminders
  • Medication Adherence: Automated check-in calls ensure patients are taking prescribed medications
  • Pre-visit Screening: Collect patient symptoms and medical history before appointments

Internal operations

  • Data Collection and Surveys: Voice agents conduct customer satisfaction surveys or gather feedback conversationally
  • Field Service Support: Technicians use voice agents to access manuals, log work, and order parts hands-free
  • Employee Services: Handle HR inquiries, time-off requests, and benefits questions for internal teams

The business value of AI voice agents

AI voice agents deliver significant business impact with measurable returns. Companies report quantifiable improvements across key metrics.

Key Benefits:

  • Cost Reduction: Scale operations without proportional staffing increases
  • Customer Satisfaction: 24/7 availability eliminates frustrating menu trees
  • Revenue Growth: Systematic lead follow-up increases conversion rates
  • Quality Assurance: Consistent service delivery and automatic documentation

Operational cost reduction

According to McKinsey estimates, applying generative AI to customer care can increase productivity by a value of 30 to 45 percent of current function costs, allowing voice agents to handle high volumes of routine calls without requiring proportional increases in staffing. This scalability transforms the economics of customer service, particularly for businesses with seasonal peaks or rapid growth. Rather than hiring and training temporary staff, companies can instantly scale voice agent capacity based on demand.

Enhanced customer experience

Customers consistently report higher satisfaction with voice agents compared to traditional IVR systems, with one 2025 report finding that 69% of companies saw improved customer service after implementing conversation intelligence. The elimination of frustrating menu trees, immediate 24/7 availability, and ability to handle complex requests in natural language all contribute to improved customer perception. Voice agents also reduce average handling time by efficiently gathering information and routing complex issues to the right human agent when needed.

Revenue acceleration

In sales contexts, voice agents ensure no lead goes uncontacted. They qualify prospects outside business hours, schedule demos at optimal times, and handle initial product education. This systematic follow-up increases conversion rates while allowing human sales teams to focus on high-value activities like closing deals and relationship building. The efficiency gains are significant; a notable case study found that implementing generative AI reduced the time agents spent on an issue by 9 percent.

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Quality and compliance improvements

Some case studies show AI agents managing as much as 77% of initial client support tiers, delivering consistent service quality because they never have bad days, always follow scripts, and automatically document every interaction. For regulated industries, this consistency ensures compliance with disclosure requirements and creates comprehensive audit trails. The ability to analyze every conversation provides insights for continuous improvement that sampling-based quality programs can't match.

Competitive differentiation

Early adopters of voice agent technology gain market advantages through superior customer experience and operational efficiency. As customer expectations evolve, the ability to provide instant, intelligent voice interactions becomes a differentiator rather than a nice-to-have feature.

How to build and implement an AI voice agent

Building production-ready voice agents requires a structured approach. Follow these proven steps for successful implementation.

Step

Key Action

Success Factor

1. Define Objectives

Pick one specific use case

Narrow scope delivers better outcomes

2. Select AI Models

Choose STT, LLM, TTS providers

Prioritize accuracy and latency

3. Choose Orchestration

Select platform for AI coordination

Match capabilities to team skills

4. Design Flows

Map conversation logic and personality

Include error recovery strategies

5. Integrate Systems

Connect to CRM, calendar, knowledge base

Enable meaningful actions

6. Test and Deploy

Start with limited release

Monitor metrics and iterate

Step 1: Define your objectives and scope

Start with a clear, specific objective. Narrow scope leads to better outcomes—it's better to excel at one use case than poorly handle many. This aligns with advice from industry leaders to 'start slowly, test, and scale' rather than trying to do too much at once. Document success metrics upfront to guide development decisions and prove ROI.

Step 2: Select your AI model stack

Choose your speech-to-text, LLM, and text-to-speech providers based on your specific requirements:

  • Speech-to-text: Prioritize accuracy and latency. According to an AI insights report, 47% of tech leaders named accuracy as a top evaluation factor when choosing an AI vendor. Look for models that handle your target demographics' accents and support necessary languages.
  • LLM selection: Balance capabilities with cost. GPT-4 offers sophisticated reasoning but may be overkill for simple FAQ responses
  • Text-to-speech: Voice quality matters—test multiple options with your target audience to find the right balance of naturalness and clarity

Step 3: Choose an orchestration platform

An orchestration platform connects your chosen AI models and manages the real-time flow of data between them. This is the framework your agent is built on. Consider factors like ease of use, customization depth, deployment options, and integration capabilities when selecting your platform.

Step 4: Design conversation flows

Map out the conversation flow, including the agent's personality, the questions it will ask, and how it should handle interruptions or unexpected queries. Create detailed dialogue trees for primary use cases, define escalation triggers for human handoff, and establish error recovery strategies. This design phase should include defining any external tools or APIs the agent needs to call.

Step 5: Integrate with business systems

Connect the agent to your essential business systems so it can perform meaningful actions. Common integrations include:

  • CRM platforms for customer data access
  • Calendaring systems for appointment booking
  • Knowledge bases for accurate information retrieval
  • Ticketing systems for issue tracking
  • Payment gateways for transaction processing
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Step 6: Test and iterate

Rigorously test the agent with real-world scenarios before full deployment. Start with internal testing, progress to beta users, then gradually expand. Voice agents improve through iteration—plan for continuous refinement based on actual usage data.

Step 7: Deploy and monitor

Launch with careful monitoring of key metrics. Track conversation completion rates, user satisfaction, escalation frequency, and technical performance indicators like latency and error rates. Use these insights to continuously refine conversation logic and improve effectiveness.

What to consider when choosing an orchestration tool

Choosing the right orchestration tool requires evaluating must-have factors against your specific requirements.

Technical Requirements:

  • Expertise Level: API/code vs. no-code solution needs
  • Customization Depth: Control over conversation design and logic
  • Deployment Model: Cloud vs. self-hosted options

Performance Requirements:

  • Latency: Real-time response speed for natural conversations
  • Integration: Connection to existing CRM and telephony systems
  • Scalability: Peak volume handling without quality degradation

The right choice depends entirely on your specific use case, team capabilities, and business requirements. Teams almost always underestimate the technical debt created by choosing platforms that exceed their maintenance capabilities. More customizable platforms offer greater flexibility but typically require more development resources to implement and maintain.

Most platforms now charge based on some combination of conversation minutes, API calls, and feature tiers. Usage-based models scale with your needs but can become unpredictable, while subscription approaches offer cost certainty. This becomes non-negotiable when advanced AI agents move from pilot to production.

Top 6 orchestration tools for building AI voice agents

The orchestration platform you choose becomes the foundation of your voice agent. Each tool takes a different approach to connecting AI models, managing conversations, and scaling applications. Here are the platforms delivering results in production today.

1. Vapi: Developer-friendly with visual design options

Vapi bridges the gap between no-code simplicity and developer flexibility. It's specifically built for the voice-agent use case, and has quickly gained traction for teams that need both visual conversation mapping and API-driven customization.

Key capabilities:

  • No-code Flow Studio for visual conversation design without coding
  • API-native architecture with programmatic access to every feature
  • Multi-language support for global deployments
  • Tool calling capabilities for integrating external data sources
  • A/B testing to optimize conversation performance
  • 1500+ integrations with third-party services

Vapi takes a unique dual approach. The visual interface helps business stakeholders map out conversation flows, while developers can access the same functionality through APIs for deeper customization. This flexibility means you can start simple and add complexity as your voice agent evolves.

Vapi natively integrates with AssemblyAI's streaming speech-to-text API to deliver the must-have low-latency transcription for natural-feeling conversations. It's a great solution for customer service applications where cross-channel consistency matters.

2. LiveKit: Open-source with maximum control

LiveKit is a fully open-source platform for building real-time media applications. LiveKit Agents builds on top of this foundation to provide tools for developers to easily build AI agents.

Key capabilities:

  • Open-source codebase you can actually modify and extend
  • Multimodal support spanning voice, video, and text interactions
  • Function calling for triggering complex actions or retrieving data
  • Turn detection that makes conversations feel more natural
  • Native telephony integration for both inbound and outbound calls
  • Rich plugin ecosystem that keeps growing with the community

Since LiveKit is open-source, you won't get locked in to third-party hosting in perpetuity. And the Agent's framework flexibility means you can build your agents and systems tailored to your use-case. Need a specific feature? Build it. Want to customize how components interact? You can.

AssemblyAI's streaming model plugin for LiveKit Agents makes it easy to convert speech-to-text in real-time. Several enterprise customers have built distinctive voice experiences they couldn't create on other platforms.

3. Daily/Pipecat: Flexible open-source orchestration

The team at Daily built Pipecat because they couldn't find an orchestration framework flexible enough for their own needs. This open-source Python framework doesn't lock you into specific vendors or approaches—instead, it offers a wide set of composable tools so developers can build how and with what they want.

Key capabilities:

  • Vendor-neutral design that works with any AI services
  • Multi-turn context management for coherent conversations
  • Real-time media transport optimized for voice and video
  • Phrase endpointing that catches natural speaking breaks
  • Multimodal support for richer interaction models
  • Completely customizable conversation workflows

Pipecat is all about flexibility. Most platforms push you toward their preferred AI providers, but Pipecat lets you mix and match components based on performance, cost, and specific requirements. Need to swap out an LLM or try a different text-to-speech engine? No problem.

The framework integrates cleanly with AssemblyAI's streaming speech-to-text model while allowing developers to control exactly how speech recognition fits into their architecture.

4. Retell: Best for natural conversation

Retell zeroes in on the biggest challenge in voice technology: making interactions feel natural. It focuses on eliminating the awkward pauses and robotic exchanges that unhinge most voice systems.

Key capabilities:

  • Proprietary turn-taking models that mimic human conversation patterns
  • Interruptibility so callers can cut in without the system breaking
  • Industry-leading low latency (responses typically under 500ms)
  • Multi-language support right out of the box
  • Deployment options for web, mobile, and telephony
  • Adaptive error recovery when conversations go off track

While other platforms treat voice as just another channel, Retell optimizes every component around creating natural dialogue flow. The system actually listens for interruptions and adapts in real-time (just like humans do).

5. Synthflow: No-code for faster deployment

Synthflow strips away the complexity of voice agent development. It's built for business teams who need functional voice agents without diving into code or managing infrastructure.

Key capabilities:

  • Complete no-code interface with drag-and-drop simplicity
  • 200+ pre-built integrations that work out of the box
  • Ready-made templates for common business scenarios
  • Multi-language capabilities with minimal configuration
  • Enterprise security features for regulated industries
  • Usage-based pricing that scales with your needs

Synthflow focuses on accessibility. Other platforms require at least some development resources, but Synthflow puts voice agent creation in the hands of business users. The template library covers everything from appointment scheduling to customer surveys to let you customize existing flows rather than starting from zero.

Synthflow offers SMBs and departments with limited IT support the fastest path from concept to deployment.

6. Bland: Self-hosted security for enterprise

Bland solves the security concerns that keep voice agents out of highly-regulated industries. This enterprise-focused platform provides complete infrastructure control without sacrificing conversation quality or features.

Key capabilities:

  • Self-hosted end-to-end infrastructure that never leaves your network
  • Human-like voice quality that maintains brand consistency
  • Custom prompts and guardrails to enforce business rules
  • 24/7 availability with built-in redundancy
  • Analytics dashboard for measuring performance and ROI
  • Warm transfer capabilities when human intervention is needed

Voice interactions contain sensitive customer information that many organizations can't legally process in the cloud. Bland keeps everything on your infrastructure: transcription, processing, and response generation all happen behind your firewall.

Financial services, healthcare, and government agencies have been quick to adopt Bland due to its enterprise-grade security.

AssemblyAI's role in the Voice AI ecosystem

Voice agents are only as good as their ability to understand what people are saying. That's where AssemblyAI's Speech Understanding models create a foundation for natural, responsive interactions.

AssemblyAI's Universal-Streaming model is purpose-built for voice agent applications, delivering industry-leading accuracy with features optimized for real-time conversation:

  • Ultra-low latency: Get immutable transcripts in ~300ms, enabling your agent to process information and respond without the awkward pauses that make conversations feel robotic.
  • Intelligent Turn Detection: A sophisticated model combining semantic and acoustic analysis accurately detects when a user has finished speaking, allowing for natural turn-taking and interruption handling.
  • Keyterms Prompting: Improve recognition for domain-specific terminology, proper nouns, and unique phrases by providing a list of key terms to the model at the start of a session.

We've designed our speech-to-text technology with flexible deployment options. Whether you're building with Vapi, LiveKit, Pipecat, or creating custom implementations, AssemblyAI provides the reliable speech recognition layer that voice agents depend on.

The platform processes millions of hours of audio monthly for voice agent applications, with consistent performance across diverse accents, background conditions, and conversation types. This production-proven reliability means you can focus on building great voice experiences rather than worrying about transcription accuracy.

Find the right tool for your voice strategy

Choosing the right orchestration platform comes down to matching capabilities with your specific requirements and technical resources. There's no one-size-fits-all tool, but you'll find an orchestration solution that fits your needs here:

  • For teams balancing speed and flexibility, Vapi offers visual design with API escape hatches
  • When maximum customization matters, LiveKit and Pipecat provide open-source foundations with lots of control for developers
  • If conversation quality is your priority, Retell's focus on turn-taking creates natural interactions
  • When you need rapid deployment without coding, Synthflow delivers results quickly
  • For strict security requirements, Bland's self-hosted approach keeps sensitive data under your control

What matters most is building on a foundation that can grow with your needs and adapt to changing technology.

AssemblyAI is pushing speech recognition forward with regular model updates that improve accuracy, reduce latency, and improve the voice experience. Start building your voice agent today with free API credits and see what's possible when you combine powerful orchestration with industry-leading speech recognition.

Frequently asked questions about AI voice agents

How are AI voice agents different from traditional IVR?

Traditional IVR uses rigid menus, while AI voice agents understand natural language and handle complex queries conversationally.

What is the most important component of an AI voice agent?

Speech-to-text is foundational—if transcription is inaccurate, all downstream processing fails. The impact of accuracy is significant, as research shows that improving from 85% to 95% accuracy can reduce transcription errors from 15 per 100 words to just five.

Can AI voice agents handle multiple languages?

Yes, but all components (STT, LLM, TTS) must support the desired languages.

How long does it take to build and deploy a voice agent?

Simple agents deploy in days using no-code tools; complex systems take weeks to months including testing.

Do I need to build my own AI models for voice agents?

No, most implementations use pre-built models through APIs, significantly reducing complexity and time to market.

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