Introduction
When building a voice AI agent, it's not enough that it simply works. It needs to understand context, sound natural, and stay consistent across every interaction. That's where voice AI evaluation comes in—measuring how well your AI performs in real conversations, not just scripted demos.
In this white paper, we'll compare Vapi Evals and Future AGI Evals, two leading approaches to voice agent testing and optimization. While Vapi Evals are great for quick, transcript-level checks within the Vapi ecosystem, Future AGI Evals go deeper—simulating real conversations, analyzing tone and naturalness, and providing comprehensive AI agent benchmarking across multiple providers.
TL;DR:
Use Vapi Evals for quick, transcript-level testing inside the Vapi dashboard.
Use Future AGI Evals for large-scale, simulation-based, cross-provider reliability testing.
Table of Contents
Before We Begin
Chapter 1: What Are Evals and Why Do You Need Them?
Chapter 2: Understanding Vapi: The Voice Infrastructure Layer
Chapter 3: Understanding Future AGI: The AI Engineering and Optimization Platform
Chapter 4: Vapi Evals: What They Offer
Chapter 5: Future AGI Evals: What Makes Them Different
Chapter 6: How Vapi and Future AGI Stack Up
Chapter 7: Final Thoughts
Before We Begin
Before diving into the comparison, it's worth understanding what voice AI evaluation really means and why even well-built agents often fail in real-world use. Voice AI that sounds flawless in a demo often fails in the wild—accent variation, background noise, or tone mismatches can break user trust. That's why voice agent testing matters: it turns subjective impressions li
Chapter 1: What Is Voice AI Evaluation and Why Do You Need It?
When users talk to a voice AI, they form an impression in seconds, not based on how smart it is, but how human it feels. That’s why Voice Evals are essential. They measure not just correctness, but experience.
Let’s look at what happens when voice agents aren’t properly evaluated.
Case 1: Misheard Details Cost Real Customers
A fintech startup launched a phone-based assistant to help users reset passwords and verify transactions. During live calls, the agent frequently misheard account numbers and names, especially from speakers with regional accents or background noise.
Users got locked out, agents had to step in, and complaint tickets spiked within days.
Why it failed: Testing never included accent, noise, or microphone variability.
What Evals would’ve shown: Speech recognition drift and accent bias before deployment.
Case 2: Accurate But Emotionally Flat
A healthcare company built a virtual nurse to handle appointment reminders and patient follow-ups. It delivered perfect information, but in a cold, robotic tone that made patients hang up early.
The agent’s metrics looked fine in text logs, but actual conversations revealed low empathy scores and shorter call durations.
Why it failed: Evaluation focused only on correctness, not tone or delivery.
What Evals would’ve shown: Low naturalness and emotional mismatch hurting engagement.
Case 3: The Regression Nobody Caught
A support bot that had been performing flawlessly suddenly started cutting off users mid-sentence after a model update. The logic hadn’t changed, only the LLM version.
Because the team lacked automated regression evals, the issue reached production, causing hundreds of failed calls before it was traced back to a timing mismatch in the response flow.
Why it failed: No automated evaluation loop after LLM updates.
What Evals would’ve shown: Early detection of response timing regression.
Evals measure how your agent performs across key dimensions such as:
At their core, voice evals look at how your agent performs across five key dimensions.
Intent Accuracy: Does the agent correctly understand what the user means, even with natural variation in speech, tone, or accent?
Response Relevance: Are its answers contextually correct, helpful, and aligned with the conversation’s goal?
Conversational Coherence: Does it maintain a natural flow, stay on topic, and handle follow-ups or interruptions smoothly?
Speech Naturalness: Does the voice sound expressive and human, with appropriate pacing and tone for the situation?
Reliability and Consistency: Does it perform with the same quality across different inputs, users, and model updates?
Without proper evals, you’re essentially guessing how well your agent performs.
At Future AGI, evals aren’t just about assigning a score, they uncover why an agent behaves the way it does. By combining transcript-level and audio-native analysis, Future AGI helps teams pinpoint which stage of the pipeline (STT, LLM, or TTS) caused a performance drop, compare providers side-by-side, and continuously improve agent quality across every interaction.
Why Voice Evals Are Becoming Critical
As voice AI moves from demos to production, expectations have shifted from it works to it works reliably. Three big changes are driving this:
Mass Deployment: Thousands of agents are now live across industries. Without systematic evaluation, it’s impossible to detect where they fail, from noise and accent drift to tonal mismatch.
Complex Pipelines: Modern systems mix multiple STT, LLM, and TTS providers. Evals are the only objective way to compare combinations for clarity, reasoning, and realism.
Reliability as a Differentiator: Continuous evaluations catch regressions, tone breaks, or reasoning errors before real users experience them.
In short, Voice Evals have become the quality backbone of modern voice AI.
Voice Evals turn these blind spots into measurable data, letting teams test for real-world variation before it costs them user trust or brand credibility. Now that we’ve seen why evaluations are essential, let’s look at how today’s leading platforms - Vapi and Future AGI, approach them differently.
Chapter 2: Understanding Vapi: The Voice Infrastructure Layer
Vapi is a platform built for real-time voice AI. It handles the orchestration of STT (speech-to-text), LLM reasoning, TTS (text-to-speech), and telephony integration, letting you focus on conversation design rather than infrastructure.
In short, Vapi powers the call, managing connections, audio streams, and integrations seamlessly.
Recently, Vapi introduced Vapi Evals, a simple way for developers to test how their agent performs in a simulated or real voice interaction. Vapi generates transcripts and call recordings and provides call-analysis/eval features for transcript-level checks and quick debugging inside the Vapi dashboard, great for validating call flows and short scenarios, but focused primarily on transcript and call-level insights rather than large-scale audio simulation or cross-provider benchmarks.
Image 1: Vapi Evals Dashboard Interface Overview
Vapi’s evals provide quick transcript-level checks. However, they evaluate mainly what was said, not how it sounded. There’s no deep analysis of tone, naturalness, or expressiveness.
Image 2: Vapi Evaluation Editor with Test Configuration
Vapi is excellent for real-time call orchestration, it runs the pipelines that make voice agents possible. Its new evals feature helps developers check basic conversational accuracy, but it remains limited to transcript-level scoring. For deeper analysis, simulation, or cross-provider comparison, you’ll need a dedicated evaluation platform like Future AGI.
Chapter 3: Understanding Future AGI: The AI Engineering and Optimization Platform
Future AGI is an end-to-end platform for simulation, evaluation, observability, and reliability protection in AI agents. It’s built around one central idea - great AI agents are powered by great evaluations. Instead of handling calls, Future AGI connects to your existing providers like Vapi, Retell, or your own agent through a simple API key.
Image 3: Future AGI Agent Configuration Interface
Once connected, it continuously collects evaluation data, simulates conversations, and surfaces insights that help you improve reliability and user experience.
Image 4: Future AGI Observe Dashboard Project Overview
Think of the relationship this way:
Vapi runs the conversation.
Future AGI measures and improves its quality.
After connecting your agent, Future AGI automatically captures detailed performance data across recognition, reasoning, and speech stages. Every conversation is logged with transcripts, audio, and quality metrics so you can evaluate accuracy, grounding, and naturalness in one dashboard.
You can simulate thousands of conversations, evaluate audio quality and coherence, and track real-world performance through a unified analytics view. Each agent has its own workspace where teams can replay interactions, inspect reasoning flow, and spot exactly where quality dropped.
Image 5: Future AGI Performance Analytics Call Logs View
This level of depth helps teams move beyond surface-level monitoring to data and eval-driven refinement, using real interactions to run targeted evaluations, fine-tune prompts, or improve voice performance with precision.
Chapter 4: Vapi Evals: What They Offer
Vapi Evals give you the ability to quickly test and debug agents built on the Vapi platform. You can check how responses sound, replay calls, and catch basic functional issues before pushing updates.
Pros
Native integration: Works instantly with existing Vapi agents, making setup fast and simple.
Quick functional validation: Ideal for checking short conversations or confirming logic changes before deployment.
Cons
Surface-level evals only: Measures conversational correctness but not voice quality, tone, or realism.
Transcript-based evaluation: Generates transcripts and model-scored summaries that help verify if responses match expected behavior. Scores are at the transcript/response level
Limited ecosystem: Works only with Vapi-built agents; cannot test or benchmark those running on Retell or custom pipelines.
No stage-level visibility: Lacks breakdowns across STT, reasoning, and speech synthesis stages, making it hard to trace why an error occurred.
Dependent on real calls: Large-scale evals using live calls can consume telephony minutes; for high-volume testing teams should account for minutes/costs.
No cross-provider comparison: Vapi’s analysis is tied to calls processed through the Vapi platform, it’s not a cross-provider benchmarking engine.
Cannot be integrated in CI/CD
Vapi Evals are best suited for basic functional checks, confirming that an agent’s logic and response flow behave as intended. But once your testing needs extend to audio quality, user experience, or large-scale reliability, you’ll need a more advanced evaluation platform like Future AGI, which runs simulation-based, audio-native evals without relying on real calls and adds cross-provider insight at scale.
Chapter 5: Future AGI Evals: What Makes Them Different
Future AGI is more than a testing tool, it’s a full end-to-end platform where evaluation is the core engine that powers simulation, observability, regression protection, and continuous improvement. Rather than treating evals as an add-on, Future AGI embeds them into every phase of the lifecycle so teams can simulate realistic conversations, run audio-native tests, detect regressions automatically, and instrument production with meaningful signals.
Below we explain the platform capabilities that flow from this architecture and why treating evals as the engine changes how teams build and operate voice agents.
1. Simulation-Driven Evaluation
Traditional evals depend on live calls, which makes large-scale testing slow and expensive.
Future AGI replaces that with simulation-based evals, allowing you to recreate thousands of realistic voice interactions, accents, background noise, interruptions, emotion shifts, or off-script turns, without consuming real call minutes. Simulated audio-native runs are designed to avoid consuming production telephony minutes and enable statistically significant sampling.
Image 6: Future AGI Scenario Configuration with Flow Diagram
These audio-native simulations let you measure voice quality and conversational stability in a controlled, repeatable environment. Teams get statistically reliable insights that mirror real-world performance, before agents ever go live.
The interface below shows how teams select and configure voice scenarios for large-scale simulation.
Image 7: Future AGI Execution Results with Performance Metrics
After simulation runs, Future AGI provides detailed playback and evaluation insights — including recordings, transcripts, and per-eval results, to help teams analyze performance and quality metrics at every turn.
Image 8: Future AGI Call Recording and Transcript Analysis
2. Benchmarking Voice Models to Find What Works Best for Your Use Case
Future AGI evaluates agents across the entire voice-AI pipeline - from Speech Recognition (STT) to Reasoning (LLM) to Speech Output (TTS), and does so across multiple providers.
With this setup, teams can:
Compare model reasoning performance (GPT-4, Claude, Gemini, etc.) for accuracy, grounding, and coherence.
Identify the optimal STT + LLM + TTS combination for specific use cases.
Benchmark end-to-end performance across Vapi, Retell, or custom stacks through direct API connections.
Examine per-stage metrics that isolate how each component of the pipeline contributes to overall quality.
Image 9: Future AGI LLM Tracing Performance Graphs
Image 10: Future AGI Test Analytics Breakdown by Scenario
Unlike Vapi Evals, which work only within Vapi, Future AGI delivers cross-provider benchmarking so you can pick the most reliable stack for production.
3. Root-Cause-Aware Evaluation
Knowing that something failed isn’t enough; knowing why it failed is what drives improvement.
Future AGI’s Agent Compass groups similar failures, highlights the exact turn where the issue occurred, and provides actionable recommendations to pinpoint whether an error arose in STT, reasoning, or speech synthesis.
Image 11: Future AGI Agent Compass Root Cause Analysis
4. Continuous Evaluation Integrated into Your CI/CD Pipeline
Every time your team updates a prompt, swaps an LLM, or adjusts TTS parameters, Future AGI integrates directly with your CI/CD pipelines. It supports both scheduled and automated test runs, allowing teams to replay evaluation sets after each update and catch regressions before they reach production. This ensures that every model, prompt, or voice change maintains consistent reliability over time.
Image 12: Future AGI System Metrics Dashboard Overview
Pros
Simulation-first approach that replaces manual QA with scalable, audio-native testing
Cross-provider benchmarking for objective quality comparison across Vapi, Retell, and custom pipelines
Root-cause insights through Agent Compass that show exactly what went wrong and why
Continuous regression detection that safeguards performance over time
Comprehensive metrics covering clarity, tone, naturalness, and conversational stability
Cost-efficient at scale, since no live call minutes are consumed
Cons
Slightly steeper learning curve for non-technical users at setup.
Best suited for teams ready to do series testing rather than one-off checks.
In short, Future AGI Evals transform evaluation from a checkbox task into a continuous improvement cycle. They don’t just tell you whether your agent works, they explain how well it performs, why it behaves that way, and what to fix next so every conversation sounds consistent, confident, and human.
Now that we’ve seen how both eval systems work, here’s a quick side-by-side look at how they compare across key areas.
Chapter 6: How Vapi and Future AGI Stack Up
Here’s how the two platforms compare when it comes to the metrics that matter most for building reliable voice AI.
Evaluation Criteria | Vapi Evals | Future AGI Evals |
|---|---|---|
Evaluation Framework (baseline testing) | ⭐ | ⭐⭐⭐⭐⭐ |
Simulation / Large-scale voice agent testing | ❌ | ⭐⭐⭐⭐⭐ |
Multi-modal audio + voice awareness | ❌ | ⭐⭐⭐⭐⭐ |
Cross-provider / STT-LLM-TTS pipeline support | ⚠️ | ⭐⭐⭐⭐⭐ |
Root-cause analytics & diagnostics | ❌ | ⭐⭐⭐⭐⭐ |
Continuous observability | ❌ | ⭐⭐⭐⭐ |
Scalability (thousands of tests, edge-cases) | ❌ | ⭐⭐⭐⭐⭐ |
In summary, Vapi Evals focus on fast, in-platform validation; Future AGI Evals extend into simulation, cross-provider analytics, and ongoing quality tracking.
Conclusion
Vapi is excellent at what it does, hosting production voice calls with low latency and high reliability. It’s the infrastructure and orchestration platform that powers how voice agents run in real time.
Future AGI, on the other hand, is the end-to-end voice-AI testing and optimization stack, built to evaluate how those agents perform before they ever reach production.
If your users depend on your voice AI, whether for customer support, sales, or healthcare, you need an evaluation process that scales with your ambitions.
Future AGI gives teams the ability to:
Test at scale: Run thousands of realistic voice scenarios in minutes.
Automate reliability: Catch regressions instantly after each model or prompt change.
Diagnose with precision: Agent Compass pinpoints where and why quality dropped.
It’s not about choosing between Vapi or Future AGI, they serve different stages of the voice-AI journey. Vapi powers real-time conversations; Future AGI ensures those conversations stay consistently great.
As teams scale, evaluation becomes the foundation of reliability, and Future AGI helps you measure, simulate, and perfect every interaction before it ever reaches your users.
If you’re serious about delivering human-like voice experiences, explore how Future AGI Evals can help you test, simulate, and optimize with confidence. 👉Read our docs or Book a quick demo to see it in action.
