February 2026 Summaries

An IVR testing tool automates the validation of Interactive Voice Response systems and voice AI agents by simulating realistic user conversations at scale, addressing the limitations of manual testing which is slow, expensive, and unable to thoroughly test edge cases or system behavior under load. These tools are essential for maintaining quality in production voice systems, enabling automated regression testing, load testing, and adversarial testing to ensure that systems perform under realistic conditions and identify potential bottlenecks before they impact customers. By integrating with CI/CD pipelines, they facilitate continuous testing and faster iterations, allowing teams to execute thousands of test scenarios per hour, catch regressions quickly, and validate system performance, all while providing detailed reporting and failure analysis. Despite their capabilities, IVR testing tools should be complemented with voice observability and AI agent evaluation to ensure comprehensive quality assurance, as they may not catch novel edge cases or assess semantic quality nuances. Platforms like Coval offer infrastructure to streamline the setup and operation of these tools, significantly reducing the time and resources needed to implement effective IVR testing strategies.

In the realm of voice AI, turn detection, which determines when a speaker has finished their turn, is a critical yet challenging component due to its timing sensitivity and dependence on user behavior and environment. The complexity of turn detection arises from its multi-layered system comprising Voice Activity Detection (VAD), endpointing, and semantic turn detection, each contributing its own potential failure modes. Effective testing of turn detection requires a systematic approach that goes beyond ad hoc manual calls, involving the simulation of diverse user personas and environments to capture a wide array of potential issues such as interruption handling, long pauses, and background noise challenges. Metrics such as interruption rate, response latency, and the frequency of reprompting are essential for assessing turn detection quality. Continuous testing is crucial to identify configuration changes or model updates that might degrade performance, and platforms like Coval offer tools to simulate and measure these scenarios effectively.

Building a HIPAA-compliant voice AI agent for healthcare poses significant challenges, primarily due to the strict regulations surrounding the handling of Protected Health Information (PHI). The process requires careful selection of vendors who sign Business Associate Agreements (BAAs) and adhere to requirements such as encryption in transit and at rest, access controls, and audit logs. Voice AI systems must ensure compliance by encrypting audio data, managing PHI in transcription and text-to-speech processes, and securing data during interactions with large language models (LLMs) and third-party systems. Different architecture patterns, such as fully managed cloud services or self-hosted systems, offer varying degrees of data control and operational complexity. Compliance verification involves continuous testing of PHI handling, identity validation, and minimal necessary disclosure, while also considering related standards like SOC 2 and GDPR for international operations. With the right infrastructure and testing frameworks, such as those provided by Coval, healthcare organizations can maintain compliance and protect patient data effectively.

In 2026, the choice between Vapi and Retell AI for building voice AI agents hinges on the subtle yet significant differences between the two platforms, despite their shared core value of providing fast infrastructure for conversational voice agents without the need for building complex components from scratch. Both platforms offer audio orchestration, developer-first APIs, and flexibility in choosing STT/LLM/TTS stacks, allowing for rapid development from concept to demo. While Vapi's strength lies in its code-first flexibility, offering extensive customization and a vibrant developer community, Retell excels with its visual flow builder that caters to teams seeking structured flows and pricing transparency. The decision largely depends on a team’s working style and specific requirements, with Vapi suiting engineering-heavy teams needing maximum flexibility and Retell appealing to teams that prioritize visual conversation design and faster iteration. Additionally, using tools like Coval for objective vendor comparison and bakeoffs can aid in making an informed decision by providing comprehensive testing and monitoring capabilities, ensuring the chosen platform aligns with the team's needs and delivers expected performance in production.

Echo cancellation is a critical challenge in voice AI systems, as the technology struggles to differentiate between genuine user input and the system's own text-to-speech (TTS) output, leading to conversational disruptions. Unlike traditional telephony, where echo is merely an auditory annoyance, in voice AI, it can corrupt the input pipeline, causing the agent to respond to itself in a loop. This issue is exacerbated in environments with reflective surfaces or when using devices with poor hardware echo cancellation, such as some Android phones. WebRTC's built-in Acoustic Echo Cancellation (AEC) can mitigate these issues to some extent, but its effectiveness varies across browsers and devices, with Firefox often delivering poorer performance. Architectural solutions like server-side echo cancellation, audio ducking, and barge-in detection with echo awareness can help, though they may introduce latency or limit user interaction capabilities. Testing for echo scenarios is complicated due to the need for physical audio setups, but production monitoring for echo indicators such as conversation loops and high interruption rates can help identify issues. Requiring or detecting headphone usage remains the most reliable method to prevent echo, though it's not always feasible in consumer applications.

Bland AI is a voice orchestration platform designed to facilitate high-volume outbound phone automation, enabling enterprises to scale their voice outreach rapidly without building infrastructure. The platform integrates speech-to-text, large language models, and text-to-speech into a pipeline optimized for managing up to 20,000 concurrent calls per hour, providing services such as WebRTC streaming and conversation state management. Its core features include a developer-friendly API, built-in voice cloning, and a visual builder for creating structured Conversational Pathways, which ensure consistent call flows and reduce deviations from the script. While ideal for outbound campaigns like sales or appointment reminders, Bland AI's architecture is less suited for inbound support or scenarios requiring the lowest latency. The platform has attracted significant investment, highlighting confidence in its ability to handle large-scale voice automation. However, for comprehensive testing and quality assurance, many teams complement Bland with specialized platforms like Coval, which provide large-scale simulation and production quality monitoring to enhance Bland’s infrastructure capabilities.

In the latest episode of "Conversations in Conversational AI," Neil Zeghidour, CEO and co-founder of Gradium and co-founder of Kyutai, discusses the innovative developments in speech-to-speech models that are reshaping the future of voice AI. Zeghidour's journey began with the establishment of Kyutai as a nonprofit research lab, emphasizing the importance of risk-taking in breakthrough innovations, which eventually led to the creation of Gradium for commercial product development. Key advancements include the Moshi project, which introduced full duplex conversations, allowing simultaneous speaking and listening without traditional turn-taking constraints, achieved through audio language models instead of diffusion models. Despite these advancements, challenges remain due to the intelligence gap between speech-to-speech and text models, largely due to differences in training data and inherent distractions in audio data. While cascaded systems currently dominate, offering modularity and steerability, Zeghidour predicts a shift towards more natural, expressive interactions that capture the complexities of human conversation. The episode also highlights the potential of miniaturized models like Pocket TTS for efficient on-device processing and explores the future applications of voice AI in robotics and spatial audio environments, where current models struggle to adapt.

Retell AI is a voice orchestration platform designed to expedite the creation of conversational voice agents by managing complex infrastructures such as WebRTC audio streaming, telephony integration, and real-time conversation orchestration. It allows developers to configure voice infrastructure with sub-second latency and offers flexibility in choosing providers for speech-to-text (STT), language models (LLM), and text-to-speech (TTS). Retell excels in rapidly prototyping voice agents, making it ideal for teams looking to validate concepts quickly without investing in building foundational infrastructure. However, its abstraction layer prioritizes developer velocity over deep customization, which might limit teams needing proprietary models or comprehensive control over infrastructure. Retell’s built-in tools offer functional validation and operational monitoring, but for extensive production quality assurance, integrating specialized platforms like Coval can complement its capabilities by providing large-scale simulation and detailed quality evaluation. While Retell suits standard use cases and budgets accommodating component-based pricing, projects necessitating proprietary technology, non-technical teams, or extensive control over infrastructure might consider custom solutions.

When selecting a text-to-speech (TTS) provider for voice AI projects, the choice between ElevenLabs and Cartesia hinges on specific use case needs, with both platforms excelling in distinct areas. ElevenLabs offers a comprehensive AI audio platform with exceptional voice quality, prosody, and multilingual capabilities across 70+ languages, making it ideal for content creation, global reach, and long-form audio projects. The platform supports various audio needs, including text-to-speech, speech-to-text, dubbing, and more, with a credit-based pricing model. Cartesia, on the other hand, is optimized for real-time conversational AI, focusing on ultra-low latency and real-time performance, making it suitable for voice agents in customer support and interactive applications. It supports 15 languages, provides unlimited voice cloning, and offers cost-effective pricing designed for high-volume deployments. While ElevenLabs excels in quality and breadth, Cartesia stands out in speed and efficiency, allowing users to choose based on their priorities for audio capabilities or real-time performance. Both platforms can be complemented with specialized testing and monitoring platforms like Coval for enhanced quality assurance, ensuring reliable voice AI performance in diverse real-world conditions.

Multi-agent voice AI systems, while appealing in theory due to their modularity and specialization, often encounter significant issues during real-world deployment, leading to frustration and frequent cancellation of projects. These systems face challenges such as coordination breakdowns, context loss in lengthy conversations, hallucination cascades where agents reinforce each other's errors, compounded latency, and a gap between demo and production performance. Effective solutions include using a central orchestrator to maintain conversation context, implementing hierarchical memory to manage information, verifying facts against source systems, reducing latency by parallelizing agent tasks, and conducting comprehensive testing with diverse scenarios to ensure systems work under various conditions. Observability is crucial for identifying and resolving issues quickly, and teams are advised to start with a single capable agent and add complexity only when justified. Success in these projects hinges on robust testing, monitoring, and improvement infrastructures rather than the intelligence of the AI itself.

Voice AI observability provides real-time visibility into voice interactions, transforming production systems from opaque operations into insightful, learning systems. It captures four critical data categories: conversation content, audio recordings, context signals, and outcome data, which aid in understanding and improving voice AI systems by identifying root causes of failures, user sentiment, and system performance bottlenecks. Unlike traditional monitoring, which focuses on system health, voice observability emphasizes conversation quality, enabling proactive improvements and systematic debugging. Teams typically progress through four maturity levels of observability, from minimal visibility to intelligent observability with automated quality scoring and anomaly detection. The decision to build or buy observability infrastructure depends on resources and requirements, with platforms like Coval offering turnkey solutions that expedite implementation and continuous improvement. Observability not only enhances quality monitoring and root cause analysis but also ensures privacy and compliance with data protection regulations. The investment in observability infrastructure offers significant ROI by reducing incidents, expediting debugging, and boosting deployment confidence and quality improvements.

Many voice AI deployments lack proper evaluation infrastructure, leading to widespread industry issues with quality and reliability. Without tools and processes for voice observability, AI agent evaluation, voice AI testing, and continuous improvement, teams often discover problems through customer complaints rather than systematic measurement, resulting in costly firefighting and invisible quality degradation. Despite the common misconception that successful demos indicate production readiness, these systems require a robust evaluation framework that spans all stages of deployment to ensure effective performance in real-world scenarios. Key obstacles include false confidence from demos, lack of clear ownership, the complexity of voice evaluations compared to text, and historically limited testing tools. Investing in comprehensive voice AI evaluation infrastructure can significantly reduce incidents, improve resolution rates, and enhance overall customer experience, with a typical return on investment ranging from 5-20x within the first year.