Evaluating Conversational AI: Why Accuracy Isn't Enough
Simple Q&A voice bots with single-turn queries and clean audio? Accuracy metrics are enough. This is for teams handling multi-turn conversations, retrieval-augmented workflows, or users calling from noisy environments where accuracy alone stops telling the full story.
Quick filter: If the transcript “looks right” but users still hang up, accuracy isn’t your problem.
Conversational AI has advanced dramatically in a short amount of time. What used to be simple IVRs and scripted flows are now LLM-powered voice agents capable of multi-step reasoning, memory, and retrieval. On the surface they sound fluent, fast, and confident.
But fluency is not reliability.
Accuracy used to be my primary evaluation metric. Then I watched agents score 95%+ on benchmarks and fail catastrophically in production. The disconnect has a name—we started calling it the "fluency trap" after seeing it repeat across deployments. The agent sounds right. The transcript looks right. But the user didn't get what they needed.
Most evaluation frameworks still focus on a single metric: accuracy. "Did the voice agent answer the query correctly?" This framing ignores how voice agents actually behave. A voice agent can sound realistic and deliver a response that sounds correct while reasoning incorrectly, retrieving the wrong information, or missing critical contextual cues.
Recent research reinforces this fluency–reliability gap. Some studies show that conversational AI can produce answers that sound correct but quietly miss critical details, context, or grounding.
Other studies show that these failures occur because accuracy only measures whether an output “looks right,” not whether the system retrieved the right information, reasoned correctly, or communicated clearly. In production environments such as healthcare, finance and logistics these gaps widen fast.
| Dimension | What to evaluate | Why accuracy misses it |
|---|---|---|
| Audio and ASR | Noise, accents, misrecognition | Transcripts can look correct but be wrong |
| Retrieval alignment | Right info, right depth | Accurate docs can still be irrelevant |
| Multi-turn reasoning | Context and state over turns | Single-turn accuracy ignores drift |
| User experience | Latency, pacing, interruptions | Correct answers can still feel broken |
Why Accuracy Breaks Down in Real Conversations
Voice agents operate across multiple layers, audio input quality, ASR stability, retrieval alignment, prompt design, and multi-turn reasoning and every one of those layers can distort the final output.
A voice agent can generate answers that sound correct but skip important details, or drift. A single misheard noun in the ASR pipeline can invert the meaning of an instruction. A voice agent may answer the intended question but quietly skip over a guardrail. Retrieval pipelines may surface content that is technically accurate but operationally irrelevant for what the user needs next.
None of these breakdowns are accuracy failures. They’re system-behavior failures: issues rooted in how the agent listens, interprets, retrieves, reasons, and responds across turns. But if accuracy is only measured as the final answer, there is no visibility into the layers where conversational reliability is actually determined.
The Blind Spots of Accuracy-Based Evaluation
Accuracy is a narrow metric. It captures whether the final output was correct but does not account for the conditions under which that answer was produced. This leaves several important behaviors unmeasured:
Voice User Experience
Users don’t judge the quality of a conversation only by whether the answer was technically correct. They judge it by whether the interaction felt clear, natural, and aligned with their intent. A response can be accurate and still fail if it’s delivered too slowly, too quickly, with poor pacing, or in a way that forces the user to repeat themselves.
These breakdowns never show up in accuracy metrics because the “right answer” was produced. But the interaction still fails, not because the model was wrong, but because the user experience was frustrating.
Retrieval Behavior
Retrieval is not a binary pass/fail. A voice agent can pull the correct underlying information from the knowledge base and still present the wrong detail level, outdated content or irrelevant information.
In clinical and financial workflows, this often looks like an answer that technically reflects the source but omits the constraints, warnings, or ignores the edge cases that matter. A retrieval layer can be accurate and still produce the wrong outcome.
Reasoning Over Multiple Turns
A lot of conversations aren’t single-turn queries, they involve multi-turn queries. Callers change their minds, jump between contexts and can change their instructions. Evaluating a single output tells you nothing about how well the agent maintains coherence over time. Multi-turn reasoning failures rarely appear in accuracy metrics, but they are some of the most damaging breakdowns in production.
The Failure Modes Teams Miss
Audio quality is the first blind spot. Users call from cars, warehouses, sidewalks, and Bluetooth headsets. ASR behaves differently in every one of those environments, and a single misheard word can corrupt the entire interaction. These issues never show up in text-based accuracy tests.
In our recent conversation with Fabian Seipel from ai-coustics, he emphasized how real-world audio introduces distortions most teams never test for, including compression artifacts, clipped speech, mic inconsistencies, and overlapping voices. Voice agents often fail at the audio layer long before the model ever generates an answer.
ASR Hallucinations
ASR hallucinations are one of the most damaging and most invisible failure modes. The transcript looks fluent, grammatical, and perfectly reasonable, but it doesn’t reflect what the user actually said. Because the text “looks correct,” downstream components treat it as ground truth.
From there, the entire conversation shifts.
The language model reasons over the wrong input. Retrieval fetches passages that match the hallucinated text, not the original utterance. Guardrails can trigger on the wrong entities and constraints are applied to the wrong values. By the time the user realizes the system misunderstood them, the agent has already committed to a faulty path.
Retrieval Failures
Retrieval failures are equally subtle and just as common. Retrieval is not a monolithic “correct or incorrect” step, it’s a layered behavior with many points of drift:
- The agent may retrieve the right document but the wrong section.
- The system may surface content that is factually correct but irrelevant to the user’s intent.
- It may return an outdated version of a guideline that is no longer actionable.
- It may provide far more detail than the user can process, or far less than they need.
In clinical and financial workflows, these failures are particularly costly. A response can technically reflect the source material while omitting the constraints, warnings, exclusions, or thresholds that determine safe action. The model “did retrieval correctly,” but the interaction is still misleading.
Accuracy treats retrieval as a pass/fail event. In reality, retrieval quality is about alignment:
Does the information surfaced actually support the user's goal at that moment?
A retrieval layer can be perfectly accurate at the document level and still produce the wrong outcome.
What a Modern Evaluation Stack Should Look Like
Conversational AI must be evaluated as an integrated system. That means measuring how the agent hears, interprets, retrieves, reasons, and responds over the entire conversation.
Accuracy becomes one metric among many. User experience metrics capture clarity and pacing. Retrieval metrics capture contextual alignment, not just correctness. Comprehensive testing and monitoring captures how the voice agent behaves when audio or phrasing is imperfect. Multi-turn evaluation captures coherence over time rather than correctness in isolation.
Research is moving in this direction, toward multi-dimensional evaluation frameworks that combine user experience, information retrieval behavior, and multi-turn reasoning. Accuracy still matters but it reflects only one dimension. Modern conversational AI requires testing tools that account for how people speak, how context shifts, and how each layer of the pipeline affects the agent's behavior across the conversation.
Flaws but Not Dealbreakers
Multi-dimensional evaluation isn't free. Some honest limitations:
You can't measure everything. Voice user experience has subjective components that automated metrics struggle to capture. We're still figuring out how to automatically detect "this felt awkward" versus "this felt natural."
Retrieval evaluation requires ground truth. Measuring whether the right information was surfaced assumes you know what the right information was. For open-ended queries, this gets fuzzy.
Multi-turn testing is expensive. A 5-turn conversation requires 5x the test infrastructure of a single-turn test. Some teams run comprehensive multi-turn tests nightly rather than on every commit.
There's a tension between depth and coverage. You can test a few scenarios deeply or many scenarios shallowly. Different teams make different tradeoffs here, and the right balance depends on your use case.
Ready to move beyond accuracy-only evaluation? Explore how Hamming tests across the full conversation lifecycle.
