testing-benchmarks

This is related to the first point in this section but worth emphasizing as a separate benchmark if the user base is global or diverse. Test the agent on non-standard accents and dialects (including code-switching where a user might mix languages). The AI’s training data often determines how well it handles these. You might use specific datasets (e.g., Indian English, African American Vernacular English, etc.) and measure comprehension accuracy. We know that without exposure, ASR systems see higher WER on unfamiliar accents​ , so this test essentially quantifies that for your system and tracks improvements as you add more diverse training data or models like OpenAI Whisper (which is trained on many languages) in the pipeline. A target could be set, such as reducing the WER gap between standard accent and heavy accent by Z%. If the agent serves multiple languages, evaluate its language detection and switching capabilities as well (does it seamlessly handle a bilingual user?). This ensures edge cases of multilingual callers are handled.

No AI is 100% perfect, so when misunderstandings happen, the key is how the agent recovers. We should benchmark the agent’s ability to detect and correct misunderstandings. For example, if the agent hears something incorrectly (STT error) and gives an odd answer, does it recognize confusion (perhaps by the user’s response or by low confidence) and correct course? We can simulate misunderstandings: provide an input where we know the ASR or NLU is likely to err and see if the agent either asks for clarification or uses an alternate strategy (maybe the system prompt instructs the agent to double-check when not confident). A good metric is the clarification rate: how often does the agent ask clarifying questions or confirm what it heard. If it never does, it might be overconfident. If it does it in appropriate situations, that’s good. Another metric: recovery success rate – in cases where there was an initial misunderstanding, does the conversation eventually get back on track? (For instance, user: “I need support with my printer.” agent mishears “router”, starts router troubleshooting; user says “No, I said printer”; agent apologizes and switches to printer support – that would count as a recovered error). Logging these flows and analyzing them is important. We want a high recovery rate and low incidence of complete failure due to an error.

Not all users respond promptly; some might think quietly or get distracted. The agent should handle silences or long pauses gracefully. Typically, IVR systems have timeouts and will prompt the user if they don’t hear anything for a while (“Are you still there?”). In testing, include scenarios where the user is silent for an extended period or says "hold on..." and then nothing. Measure whether the agent follows the expected behavior: does it wait the correct amount of time and then give a gentle prompt, or does it erroneously hang up or say something inappropriate? One can simulate different pause lengths to find the threshold of the agent. The criteria might be that after X seconds of no input, a reprompt is given, and after Y seconds of total silence, the call ends politely. Verify these triggers. Also test if the agent can handle when a user starts talking again exactly as the agent decides to reprompt – this is tricky, but the agent should ideally detect the user’s voice and not talk over them (or quickly stop if it does). This overlaps with interruption handling but specifically focuses on user silence and the agent’s timeout logic.

Users may sometimes use abusive language or profanity towards the agent. The agent should not break character or respond in kind. Test cases with abusive language help ensure the agent follows policy (e.g., giving a standard polite response or warning, or escalating to a human). KPIs could be that the agent never uses profanity itself (unless perhaps it’s specifically designed for a very informal persona, which is unlikely in customer service) and that it responds to abuse in an acceptable way (maybe by saying “I’m here to help; I understand you’re upset” etc., as per design). Also, if abuse gets too much, does the agent know when to terminate the call or involve a supervisor? This can be part of the design and thus tested. The benchmark could be qualitative (review transcripts of abuse scenarios to ensure compliance) or even a simple flag that no matter what the user says, the agent’s emotion detection and response remains within acceptable bounds. From a performance view, also ensure that profanity in user input doesn’t degrade the ASR or NLP (e.g., some systems might censor or mask it and could lose meaning). The agent should still correctly interpret the user’s actual request even if it’s wrapped in angry words.

Natural language is full of idioms and sarcasm that can trip up literal-minded AI. Test the agent with slang terms or colloquial phrases to see if it understands the intent. For example, a user might say "I need to ditch my old plan, it's crappy" meaning they want to cancel a service – does the agent glean that, or does it get confused by the slang “ditch” or the sentiment? Jargon and domain-specific terms are also important (especially in B2B contexts – e.g., in tech support, users might use technical lingo). The agent should ideally be trained on common slang/jargon, but testing will reveal gaps. Sarcasm detection is very hard (even for humans), but you can include a few sarcastic remarks in testing to check if the agent at least doesn’t take a clearly sarcastic statement at face value. For instance, user says "Oh great, you've been so helpful" in a frustrated tone – a naive agent might thank the user, but an advanced one might detect the dissatisfaction. While fully solving sarcasm might be out of scope, the agent should at least have strategies (like politely apologizing or clarifying). These scenarios can be manually evaluated or possibly flagged by sentiment tools (sarcasm often shows mismatch between positive words and negative tone).

Evaluate the agent with users who have non-standard speech – e.g., strong regional accents, non-native speakers, speech impairments (stuttering, lisp), or atypical speaking styles. These can significantly impact ASR performance. For instance, without training, many ASR systems have high error rates on certain accents​ . Create a test dataset of such speakers (or synthesize variations using TTS and audio filters) to measure how accuracy drops. If WER or intent detection accuracy for these groups is far worse than for baseline speakers, that’s an important finding. The benchmark might be to reduce that gap over time (through model improvements or specialized handling). For speech impairments, one might measure if the agent can still catch keywords or whether it times out. A tolerant voice agent might allow slower speech or repeat back what it heard for confirmation. These edge tests ensure the AI is inclusive and effective for a broad user base, not just ideal speakers.

As part of compliance testing, ensure the system provides adequate logging and auditability. In many industries, you must be able to audit conversations after the fact for compliance issues. The testing platform should validate that conversation logs are recorded with necessary metadata (timestamps, which automated decisions were made, etc.) and that these logs themselves are stored securely. For example, healthcare AI phone agents need ongoing monitoring – organizations should audit AI conversations for potential HIPAA violations or anomalies​ . As a benchmark, one could run a batch of test call transcripts through a compliance checker (looking for any PHI in unauthorized places, etc.) and ensure no flags are raised. Essentially, the agent should pass a compliance audit in the test environment just as a human agent would. This gives confidence that deploying it won’t introduce compliance risks.

Hallucination in AI refers to the agent fabricating information that is not true or not supported by its data. In many regulated environments, providing incorrect information is a serious issue. Even in general customer service, an AI making up a policy or a detail can erode trust. We need to benchmark how often (if ever) the agent produces factually incorrect or nonsensical statements. This can be done by having a set of factual questions or scenarios where the correct answer is known and seeing if the agent’s responses match the truth. If using a generative model, one might incorporate an automated fact-checking step or an LLM-as-a-judge approach to flag likely hallucinations. For instance, ask the agent a question with no answer in its knowledge base (to tempt a hallucination) – does it gracefully say it doesn’t know, or does it make something up? Track the hallucination rate (perhaps percentage of answers that contain false information in test queries). The goal is to minimize this. In critical areas, ideally the agent should have 0 unauthorized improvisations. If any hallucination is detected in testing, that’s a bug to fix (by refining prompts, adding fallback rules, etc.). Auditing conversations for accuracy is also important in production; for testing, one might use a smaller set of queries with known answers to regularly sanity-check the AI’s responses.

Each company will have its own rules for how the AI should behave. This can range from content guidelines (the AI should not say certain phrases or must use a greeting script) to workflow rules (always verify identity before giving account details, for example). During evaluation, conversation logs should be reviewed (or automatically scanned) for policy compliance. For example, if policy says the agent must authenticate the caller with two factors before giving sensitive info, then any test scenario where the agent gives info without auth is a failure. We might set up specific tests (like the user asks for account balance without proper verification) and ensure the agent refuses. Another example: no profanity – the agent should never use inappropriate language, even if the user does. So if we abuse the agent in a test, does it remain polite? Automated scripts can check the agent’s responses against a blacklist of disallowed words. Success criteria: 0 instances of policy violations in the test suite. This overlaps with the earlier “system prompt adherence” but here it’s more about external rules and norms being followed.

As call volumes increase, the agent should scale without crashing or significantly slowing down. Test the system’s performance at high concurrency – e.g., simulate peak hour traffic with many parallel calls. Monitor CPU/memory usage, queue lengths, and any infrastructure failures (like speech recognition service not responding or the system resetting). KPIs here might include max concurrent calls supported at a given latency, or throughput (calls/hour) until degradation. Also, test long call durations to ensure no memory leaks or quality degradation over time (does a conversation lasting 30 minutes perform as well as one of 2 minutes?). Any failure (like the agent dropping the call or restarting mid-conversation) should be recorded. Essentially, this is reliability testing – the agent should be as reliable as a phone switch in a call center. Uptime and error-free transaction rates can be metrics to report.