Best Voice Agent Evaluation Tools in 2026
Voice AI agents are quickly becoming the backbone of customer support, sales outreach, and automated workflows. But unlike text-based applications, voice introduces a unique layer of complexity. Latency above 800ms breaks conversational flow. Background noise and regional accents degrade transcription accuracy. Users interrupt, change their minds mid-sentence, and express frustration through tone rather than words. Standard testing frameworks simply cannot catch these failure modes.
That is why specialized voice agent evaluation tools have become essential for any team shipping voice AI into production. These platforms help you simulate realistic conversations, measure speech recognition quality, track response latency, and monitor live calls for regressions.
This guide compares five of the best voice agent evaluation platforms available in 2026 to help you choose the right fit for your team.
1. Maxim AI
Platform Overview
Maxim AI is an end-to-end evaluation and observability platform purpose-built for teams shipping AI agents reliably. Unlike point solutions focused solely on voice monitoring, Maxim delivers comprehensive lifecycle management spanning experimentation, simulation, evaluation, and production observability for multimodal agents, including voice applications. The platform is designed for cross-functional collaboration, enabling AI engineers, product managers, and QA teams to work together within a single interface.
Key Features
Playground++ for Prompt Iteration Maxim's Playground++ enables rapid, version-controlled prompt experimentation for voice agent instructions and conversation flows. Teams can iterate on agent behavior systematically before committing to production changes.
AI-Powered Simulation Engine The simulation engine tests voice agents across hundreds of conversation scenarios and user personas before deployment. You can simulate callers with specific accents and speaking patterns, add background noise ranging from quiet offices to busy call centers, test interruptions at natural conversation points, and introduce emotional variations from patient to frustrated. All of this happens without consuming actual call minutes or requiring expensive manual testing.
Multi-Level Evaluation Framework Maxim combines automated machine evaluators with human review workflows specifically designed for voice quality assessment. The platform evaluates agents at the conversational level: did the agent accomplish the goal, ask clarifying questions appropriately, handle unexpected inputs, and maintain context across turns? A library of pre-built evaluators covers LLM-as-a-judge, statistical, programmatic, and human scoring methods, with full support for custom evaluators.
Production Observability Once deployed, Maxim's observability suite provides voice-specific monitoring with distributed tracing across the entire voice pipeline. Track call latency, success rates, abandon rates, and tool invocation accuracy. Attach raw audio files directly to traces to replay exactly what the agent heard when investigating failures. This audio-native approach is critical for understanding whether issues originate from speech recognition, language understanding, tool invocation, or response generation.
No-Code Evaluation for Product Teams Product managers can define evaluation rules, set quality thresholds, and monitor trends through custom dashboards without engineering dependencies. This cross-functional approach accelerates quality improvement cycles.
Broad Integration Support Maxim integrates natively with LiveKit, OpenAI, LangChain, LangGraph, CrewAI, Agno, and all leading agent orchestration frameworks. It also supports OpenTelemetry for seamless data forwarding.
Best For
Teams that need end-to-end lifecycle management for voice agents, from experimentation through production monitoring. Maxim is ideal for organizations building multimodal agent architectures, teams where product managers and engineers collaborate closely on agent quality, and enterprises requiring compliance features with audit trails.
2. Hamming AI
Platform Overview
Hamming AI is a voice agent testing and production monitoring platform built by a team with experience scaling ML systems at Tesla. The platform focuses on automated scenario generation and goal-based evaluation for voice and chat agents.
Key Features
Hamming pioneered automated scenario generation for voice agents, eliminating the need for manual test case writing. The platform supports speech-level analysis that detects caller frustration, sentiment shifts, pauses, interruptions, and tone changes. It natively ingests OpenTelemetry traces and provides unified observability across testing, production monitoring, and debugging. Hamming claims 95-96% agreement with human evaluators through a two-step evaluation pipeline.
Best For
Teams that want fast time-to-value with minimal configuration overhead. Hamming works well for organizations that need automated test generation at scale and goal-based evaluation rather than script-matching.
3. Roark
Platform Overview
Roark (YC W25) is an observability and testing platform specifically designed for voice AI. The platform has processed over 10 million minutes of calls and specializes in production call replay, enabling teams to test agent changes against real conversations rather than synthetic scenarios.
Key Features
Roark captures real production calls and lets you replay them against updated agent logic, cloning the original caller's voice for realistic testing. The platform tracks 40+ built-in metrics including latency, instruction-following, repetition detection, and sentiment. It integrates with Hume for emotional signal detection and offers one-click integrations with VAPI, Retell, LiveKit, and Pipecat. Failed calls automatically become repeatable test cases for regression testing.
Best For
Teams that prioritize production monitoring and real-world performance validation. Roark is particularly strong for organizations that want to turn actual failed calls into automated test suites and need audio-native debugging beyond transcript analysis.
4. Coval
Platform Overview
Coval takes a simulation-first approach to voice agent evaluation, drawing methodology from autonomous vehicle testing. The platform focuses on large-scale pre-deployment simulation with deep CI/CD integration to catch regressions before they reach production.
Key Features
Coval provides end-to-end conversation simulation with configurable scenarios covering diverse caller behaviors, accents, and edge cases. The platform integrates directly into CI/CD pipelines so every prompt change or logic update gets validated automatically. It also offers native integration with Langfuse for teams that want to combine simulation testing with open-source observability.
Best For
Enterprise AI teams that need rigorous pre-deployment testing integrated into their development workflow. Coval suits organizations where regression testing and automated CI/CD validation are priorities over production monitoring.
5. Cekura
Platform Overview
Cekura focuses on reducing the manual QA burden for voice agent teams through automated test generation. The platform analyzes agent behavior and automatically creates test cases that cover common failure modes and edge cases.
Key Features
Cekura automatically generates test scenarios based on agent configurations and conversation patterns, reducing the time teams spend writing and maintaining test suites. The platform covers standard voice evaluation dimensions including latency measurement, goal completion tracking, and conversation flow analysis. It provides structured reporting that highlights specific failure patterns and areas for improvement.
Best For
Teams with limited QA resources who need to scale their testing coverage without proportionally scaling headcount. Cekura is a good fit for organizations early in their voice agent journey that want comprehensive test coverage without building custom evaluation infrastructure.
How to Choose the Right Tool
Selecting the right platform depends on your development stage and team structure. If you need a comprehensive platform that covers the full lifecycle from experimentation through production monitoring, Maxim AI stands out as the most complete solution with its unified approach to simulation, evaluation, and observability across voice and other modalities.
For teams focused primarily on production call analysis and real-world replay testing, Roark offers deep specialization. Hamming provides strong automated scenario generation with fast setup. Coval suits CI/CD-heavy workflows with its simulation-first approach. And Cekura helps resource-constrained teams scale test coverage through automation.
The voice agent evaluation space is maturing quickly, and teams that invest in proper evaluation infrastructure catch issues earlier, iterate faster, and deploy with confidence. Whichever tool you choose, the key is moving beyond manual testing and building systematic quality assurance into your voice agent development workflow.
