You've built a voice agent. It handles your test calls perfectly. Your team loves the demo. Leadership is impressed. You deploy to production.
Then reality hits.
Users go silent when you expect responses. Background TVs trigger phantom conversations. People interrupt mid-sentence. Your transcription returns gibberish. The agent responds to questions it shouldn't answer. What worked flawlessly in your quiet office fails spectacularly in the real world.
If you're reading this, you're probably debugging one of these failures right now. Or you're smart enough to search for problems before they happen. Either way, here's what you need to know: these aren't edge cases—they're Tuesday.
Why Voice AI Breaks in Production
The gap between demo and production isn't about compute power or model quality. It's about the chaos of human conversation. Real users don't follow scripts. They pause, interrupt, mumble, and multitask. Their environments are noisy, unpredictable, and full of distractions.
Most voice AI failures happen in the input/output pipeline—before your LLM even sees the input or after it generates a response. Here are the 7 patterns that break voice agents most often, why they happen, and how to test for them systematically.
1. User Goes Silent (No Response/Timeout Handling)
Your agent asks "What's your account number?" The user puts down the phone to find their wallet. Or they're thinking. Or they're talking to someone else. Your agent waits indefinitely, times out too quickly, or worse—continues the conversation without user input.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| Fixed timeouts don't account for question complexity | Sessions ending abruptly after questions | Immediate silence (user never responds) |
| No retry logic means one silence ends the conversation | Unusually short conversation durations | Delayed responses (15-20 second pauses) |
| Poor timeout messages confuse users | High rates of "empty transcript" errors | Intermittent silence (respond, pause, respond) |
| Infinite waiting ties up resources | User complaints about "agent hung up on me" | Background activity without speech |
2. Speech Recognition Returns Garbage
Your STT engine returns empty strings, "[INAUDIBLE]", "?????", or completely wrong transcriptions. Your agent receives input it was never designed to handle and either crashes, hallucinates, or asks users to repeat endlessly.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| No validation layer between STT and agent logic | Responses that don't match user questions | Empty transcriptions |
| Missing confidence score checks | Infinite "please repeat" loops | Low-confidence results |
| No artifact filtering for STT failure patterns | Agent responses to nonsensical inputs | Special characters ("[SILENCE]", "[OVERLAP]") |
| Assumption that STT always works (expect 5-15% failure) | High retry rates on specific phrases | Repetitive characters and partial words |
3. Users Interrupt Mid-Sentence
Your agent is explaining something. The user interrupts: "Actually, wait—". Your system either ignores the interruption, creates overlapping audio, or processes both streams as one garbled input.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| No barge-in detection to stop agent speech | Audio overlap in recordings | Early interruption (within 500ms) |
| Audio buffer issues causing delayed handling | Sudden transcript truncations | Mid-sentence interruption |
| Context loss when responses are cut off | User frustration metrics spike | Late interruption (near end of response) |
| Turn-taking confusion about who speaks next | "Agent talked over me" complaints | Multiple rapid interruptions |
4. Multiple Speakers or Background Voices
A TV plays in the background. Multiple people talk at once. A child interrupts their parent. Your agent responds to the wrong voice or creates a confused mixture of multiple conversations.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| No speaker diarization to identify individuals | Responses to background conversations | Single speaker with TV background |
| No primary speaker detection to focus correctly | Context switches that don't make sense | Two people talking simultaneously |
| Background noise treated as speech by VAD | Transcripts with mixed speaker content | Side conversations during calls |
| Context mixing from multiple conversation streams | "Agent responded to my TV" reports | Varying background noise levels |
5. Minimal or Ambiguous Responses
User says "Yes" to a complex question. Says "Tomorrow" without specifying a time. Gives one-word answers when you need details. Your agent lacks context to proceed meaningfully.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| No progressive information gathering strategy | High clarification request rates | Single-word answers to open questions |
| Assumptions about response completeness | Incomplete data collection | Ambiguous temporal references ("later", "soon") |
| Missing clarification patterns for ambiguous inputs | Conversation loops without progress | Pronouns without antecedents ("that one") |
| Poor slot-filling logic for required information | User abandonment after minimal responses | Partial information provision |
6. Out-of-Scope Questions
Your scheduling agent gets asked about medical advice. Your order-taking bot receives tech support questions. Without boundaries, agents either hallucinate answers or get stuck in off-topic conversations.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| No scope boundaries defined in agent logic | Responses outside designated domain | Adjacent domain questions |
| Missing intent classification for each turn | Conversation length without task completion | Completely unrelated topics |
| Lack of graceful redirects for out-of-scope queries | Agent providing incorrect information | Attempts to expand agent capabilities |
| Prompt injection vulnerabilities from unexpected inputs | Legal/compliance violations from overreach | Social engineering attempts |
7. Background Noise False Positives
A door slams. A dog barks. Someone coughs. Your Voice Activity Detection thinks someone is speaking, processes the noise, and your agent responds to phantom input.
| Why It Breaks | Detection Signals | Testing Approach |
|---|---|---|
| Over-sensitive VAD detecting any sound as speech | Agent responses when no one spoke | Sudden noises (door slams, coughs) |
| No noise profile calibration for environment | "Could you repeat that?" without user input | Continuous background noise (AC, traffic) |
| Missing impulse noise filtering | High false positive rates in quiet environments | Non-speech human sounds (laughing, throat clearing) |
| No validation between VAD and STT outputs | STT processing non-speech audio | Electronic sounds (notifications, alarms) |
The Path from Demo to Production
Every voice AI team encounters these edge cases. The successful ones find them during testing, not from user complaints. Here's your systematic approach:
1. Accept Reality
Your demo environment is nothing like production. Test in realistic conditions with background noise, interruptions, and unpredictable user behavior.
2. Test Systematically
Don't wait for users to find edge cases. Create test scenarios for each pattern above. Run them before every deployment.
3. Monitor Continuously
Track detection signals for each edge case. Set up alerts before problems become widespread.
4. Design for Failure
Every edge case needs a graceful fallback. Silent failures and crashes destroy user trust immediately.
Testing Your Way to Reliability
The difference between a voice AI demo and a production-ready system isn't the language model—it's how systematically you test for chaos. Each edge case above is predictable, detectable, and preventable with proper testing.
Start with the edge case causing you the most pain today. Build test scenarios around it. Validate your fixes. Then move to the next one.
Voice AI that works in production isn't about perfection—it's about handling imperfection gracefully. Test for the chaos, and your users will experience the magic.
Ready to systematically test these edge cases? Hamming provides automated testing for all seven patterns above, plus real-world simulation capabilities that catch issues before production. Learn how to test voice agents systematically →
Because your users shouldn't be your QA team.
