Beyond Benchmarks: What’s Next for Voice Agent QA in 2025

This post was adapted from Hamming’s podcast conversation with Lily Clifford, CEO of Rime. Rime builds Voice AI models for customer experience, on-prem or in the cloud, powering IVAs and agents people actually want to talk to.

Building Voice Agents That Actually Work in Production

Every week, more teams are launching voice agents, from automated ordering systems to enterprise IVRs and healthcare assistants. But while it’s easy to build a voice agent that sounds good, building one that works reliably in production remains the real challenge.

Hamming’s mission is to make it extremely easy for organizations and enterprises to QA their voice agents, both pre-deployment and post-deployment.

Demos Are Easy. Deployments Are Hard.

LLM-powered tools have made it trivial to create impressive demos. A developer can “vibe-code” a voice app in an hour, but those same demos often break under real-world conditions.

Before large language models, a working demo required the same discipline as a production system, intent modeling, ASR, backend integrations. Oftentimes, this process is skipped, which creates a wide gap between what teams demo and what they actually deploy.

Speech-to-speech pipelines illustrate this perfectly. The latency numbers are improving, but reliability still lags. Agents can hang mid-call, misfire during tool calls, or hallucinate once deployed.

What Enterprises Actually Measure

Enterprise teams care less about abstract benchmarks and more about outcomes. Benchmarks can tell you how a model performs in isolation; metrics tell you how well it performs for your customers.

Typical success metrics include:

• Task completion rate: How often users achieve their goals without escalation
• Containment rate: How well the agent resolves requests autonomously
• Human-like engagement: Behavioral signals such as users saying “thank you” to the voice agent

This shift from technical benchmarks to operational KPIs marks a broader industry evolution: quality is now measured by impact, not inputs.

The Limits of Public Benchmarks

Public benchmarks are a useful baseline, but they rarely reflect real-world performance. Synthetic tests can’t replicate the unpredictability of live environments, background noise, concurrent traffic, or fluctuating latency.

The most advanced teams now pair pre-deployment testing with post-deployment analytics to measure how models generalize in production. Bridging that gap, making synthetic benchmarks a reliable predictor of live performance is one of the core challenges in Voice QA today.

Fine-Tuning, Tool-Calling, and Multi-Agent Design Patterns

Modern voice applications are increasingly modular. Some teams fine-tune smaller language models for specific parts of the call flow, for instance, address parsing, order collection, scheduling, while others orchestrate multiple agents optimized for distinct functions.

This multi-model pattern works, but it also increases QA complexity. Each fine-tuned model adds another point of failure. Without automated regression detection, one update can silently degrade performance across the stack.

Continuous evaluation is compulsory, testing every component under realistic load conditions before issues reach customers.

Why Speech-to-Speech Isn’t There Yet

The promise of end-to-end speech-to-speech is compelling: natural prosody, low latency, and fluid interaction. But reliability remains inconsistent.

Speech-to-speech agents struggle with long-form context, repeated questions, and conversational flow. Even the most advanced systems still operate in turn-taking mode, waiting for the user to finish before speaking. Agents that can interrupt, acknowledge, or react dynamically, remain out of reach.

Until that’s solved, cascading stacks (ASR → LLM → TTS) continue to dominate production deployments, offering better reliability and clearer debugging.

The Bottleneck: ASR Accuracy

Automatic Speech Recognition is still the weakest link in the voice pipeline. Background noise, accent variation, and inconsistent phrasing can derail comprehension, and everything downstream suffers.

Even with intent recovery and prompt-based correction, poor input still leads to poor outcomes.
That’s why ASR must be tested under real-world conditions, across accents, environments, and recording qualities to guarantee consistent end-to-end reliability.

Moving Beyond Benchmarks: Toward Continuous Reliability

The next stage of Voice QA is about connecting synthetic testing with real-world observability. It’s not enough to pass a pre-deployment checklist; systems must prove reliability continuously.

Hamming’s approach focuses on:

• Simulating real traffic and multilingual scenarios pre-deployment
• Comparing synthetic test outcomes to live production results
• Building analytics that capture and measure how human an agent feels

The goal is simple: if a system performs well in test conditions, it should perform just as well with real users.

What’s Next?

Voice AI is still early. There are no standard architectures, no final frameworks, and no definitive winners. That’s what makes it exciting. Every week, new design patterns emerge, from fine-tuned micro-models to hybrid orchestration layers and the QA layer is evolving just as fast.

Recent releases like Rime's Arcana v2, which expands multilingual coverage and introduces on-prem deployment, highlight how quickly the ecosystem is advancing toward production-grade reliability.

The industry is moving from benchmarks to outcomes, from demos to deployments, and from visibility to control. Reliability will be what defines the next generation of voice AI systems.

Listen to the full conversation on our new podcast, The Voice Loop with Rime.