mejbaurbahar/fagun/chatbot-voice-agent-testing

Chatbot / Voice / Conversational Agent Testing

The pipeline to test end-to-end

Input (text/speech) → intent/NLU → reasoning/planning → tool/API call → response generation → output (text/speech). Test each stage in isolation first (does STT transcribe correctly regardless of what the agent does with it?), then the full pipeline — isolating stages tells you where a failure lives instead of just that the conversation went wrong.

Multi-turn conversation testing

• Context retention: does the agent correctly reference something said 3+ turns earlier? Test with conversations long enough to exceed casual expectations, not just 2-turn exchanges.
• Context window / memory limits: what happens when a conversation exceeds the model's context window — does it degrade gracefully (summarize, drop oldest turns intelligently) or does it silently lose critical earlier information (like an established user preference or constraint)?
• Topic switching: user abruptly changes subject — does the agent follow naturally, or does it awkwardly force the old topic?
• Correction handling: user says "no, I meant X not Y" — does the agent actually update its understanding, or does it repeat the same mistake?
• Interrupt handling (voice specifically): user starts speaking while the agent is still responding — does it stop and listen (barge-in), and does it recover the conversation state correctly after being interrupted mid-sentence?

Intent recognition & entity extraction

• Test with paraphrases of the same intent (people don't say things the same way twice) — a brittle system that only handles the exact training phrasing will fail broadly in production.
• Test entity extraction with ambiguous/malformed input (misspelled city names, informal date references like "next Tuesday," numbers spelled out vs. digits).
• Test out-of-scope input explicitly — the agent should recognize when a request is outside its capability and say so, rather than confidently attempting something it can't actually do.

Fallback & error handling

• When the agent doesn't understand, does it ask a clarifying question or escalate to a human, rather than looping the same "I didn't understand" response or guessing wrong silently?
• Test the actual escalation/handoff path (to a human agent, or a documented fallback) end-to-end, not just that a fallback message exists.
• Test recovery from a bad turn — after a misunderstanding, can the conversation get back on track, or does the whole session degrade?

Speech-specific testing (STT/TTS)

• STT accuracy: test across accents, background noise levels, and domain-specific vocabulary/jargon the generic model wasn't trained on — measure word error rate (WER) on a representative sample, not just clean-studio audio.
• TTS quality: pronunciation of domain-specific terms/names, natural pacing, and correct handling of numbers/dates/abbreviations (does it say "dot" or "point" for a decimal appropriately in context?).
• Latency: for voice, response latency has a much stricter human-perceptible threshold than text chat — measure time-to-first-audio, not just full-response time.
• Barge-in and silence handling: does the system correctly detect end-of-utterance (not cutting the user off mid-sentence, but also not waiting too long after they've finished)?

Tool-calling / backend integration

Same discipline as [[llm-testing]]'s agent/tool-use section — verify the agent calls the right backend action with correct arguments and handles tool failures without hallucinating a fake success. For a booking/support bot, always verify the claimed backend action (an order placed, a ticket created) actually happened in the system of record, not just that the agent said it did.

Safety

• Test that the agent refuses/deflects appropriately for out-of-policy requests (medical/legal/financial advice beyond scope, harmful content) consistently across many phrasings, not just the obvious direct ask.
• Test that PII shared mid-conversation (by the user) isn't inappropriately echoed back, logged in plaintext, or leaked to a downstream integration beyond what's necessary.

Reporting

Log the full conversation transcript (not just the final failing turn) as evidence — conversational bugs are almost always about what happened earlier in the session. See [[bug-reporting]].