At Media Monk, we’re at the frontier of voice AI innovation. Our flagship voice agent, Phona, powers intelligent conversations for brands across industries — booking appointments, answering queries, handling objections. But as advanced as real-time AI voice technology has become, it still faces real-world limitations worth understanding.
Number Recitations: A Tricky Terrain
If a caller asks a voice assistant to recite back a number — be it a phone number, invoice ID, or tracking code — things can get clunky. While AI models can recognize digits and even say them clearly, they often struggle with:
- Pacing: Speaking too fast or slow when reciting numbers.
- Formatting: Misplacing pauses (e.g., saying “nine nine eight one seven one” vs. “nine, nine, eight, one-seven-one”).
- Repetition Requests: In natural speech, humans can repeat or clarify a digit with minimal prompting. AI models often need explicit cues like “Can you repeat that number slowly?” — and even then, the result isn’t always smooth.
This isn’t a hardware/software issue — it’s a cognitive one. AI models weren’t originally designed to focus attention on precision recitations under variable real-time conditions.
Dialects, Accents, and Pronunciation Variability
Speech models have made leaps in understanding different voices, but regional dialects, accents, and non-standard pronunciation still trip up the most advanced systems.
- Callers with heavy regional inflections or non-native English accents may be misunderstood.
- AI-generated voices can sometimes mispronounce local names, especially in multicultural contexts or when code-switching occurs.
- Context-based correction (e.g., knowing that “Marry-on” is “Marion” in Southern U.S. English) is still inconsistent.
Phona does apply some context-aware pronunciation correction, but perfection remains elusive when operating across diverse populations and real-time environments.
The Sarcasm Problem
Sarcasm remains an unsolved problem for AI — especially in voice. Without intonation awareness or deep contextual inference, sarcastic statements are often taken literally.
If a caller says, “Oh great, that’s just what I needed…” with dripping irony, most models interpret it as a positive affirmation. Worse still, they may respond with overly enthusiastic agreement, further frustrating the caller.
Until voice models can learn tonal cues at the level humans do (a field still in its infancy), sarcasm will be a challenge.
AI’s Compulsion to “Be Helpful” — Even When It Shouldn’t Be
All major AI systems are trained with a central directive: help the user. While admirable in theory, this can cause a problem in practice:
- If the AI doesn’t know something, it sometimes prefers to make something up than admit it doesn’t know. This behavior, known as hallucination, is especially risky in live voice settings. A caller might ask:
- “What’s the refund policy for purchases made at 11:59 PM on Sundays?”
If no data has been provided about that edge case, the AI might invent a policy rather than say, “I’m not sure.” It’s not being deceitful — it’s just being overly helpful. And in business contexts, that’s a problem.
Why Do AI Voice Agents Sometimes Repeat Themselves or Reset?
While most real-time AI voice agents — including Phona — operate smoothly under structured conditions, there are still edge cases where the agent can:
- Repeat the same phrase multiple times
- Get stuck in a loop
- Suddenly reset or say, “Hi, how can I help you today?” mid-conversation
These behaviors are not bugs in the traditional sense — they’re side effects of the way real-time AI conversations are orchestrated.
What Causes This?
- Incomplete or Corrupted Transcripts: Voice agents rely on real-time transcription to understand the caller. If the transcript is:
- Cut off mid-sentence
- Full of background noise
- Interrupted by a poor internet connection - the model might misinterpret it, resulting in repeated clarifications or fallback responses.
Overlapping Audio Events
If the caller and the AI speak at the same time, or if the AI is interrupted before finishing a response, it may reset or loop back to a previous state. This is especially common in aggressive testing where callers intentionally "talk over" the AI.
State Desyncs
AI agents like Phona maintain a conversation state — but if something causes the state context to desynchronize (e.g., a long silence, a malformed input, or a backend event misfire), the agent might reset itself to a default prompt as a safeguard.
Guardrail Triggering
Sometimes what looks like repetition is actually a fallback behavior. For instance, if a user asks the same question in five different ways and the AI doesn’t have a grounded answer, it might default to a pre-written response or loop to avoid hallucinating.
Too Much Model Freedom
AI voice agents need just the right balance of guardrails vs. generative creativity. If the model is allowed too much freedom without strong instruction anchors, it may "talk in circles" or try to rephrase the same helpful sentence in three different ways — thinking it’s being useful.
How Phona Handles This
At Media Monk, we’ve engineered Phona with several layers of protection:
- Transcript filtering to remove noise, filler, or contradictory instructions
- Conversation memory smoothing to prevent overcorrection or duplicate answers
- Session-level guardrails to catch repetitions and route the AI to fallback logic
Still, AI isn’t human — and when pushed into extreme or unrealistic situations, even the best models can behave unpredictably.
Just like people under pressure, AI agents can stumble. If audio drops, transcripts misalign, or the conversation goes off-topic, Phona may repeat itself or reset. It’s not “broken” — it’s a byproduct of real-time AI trying to keep the conversation on track in the face of chaos. Phona's smart design can catch these edge cases — but deliberate provocation will always surface weirdness.
Challenges with Real-Time Voice Communication
Beyond the language model itself, real-time voice AI presents its own set of operational and perceptual hurdles. At Phona, we’ve engineered several layers of intelligence to smoothen the experience — but like all voice-based technologies, it’s a work in progress.
Signal Doesn’t Always Mean Truth
Phona attempts to auto-detect contextual signals to personalize and streamline interactions. This includes:
- Caller ID (to identify returning users or link to CRM records)
- Network & Geo Data (for routing, country code formatting, timezone alignment)
- ALT Lookups (for identifying contacts and syncing with email campaigns)
While effective in most cases, these signals can also propagate errors if upstream data is inaccurate:
- VoIP softphones using SIP trunking can spoof or mask caller ID and location.
- CRM data mismatches (e.g., a mis-tagged phone number) can lead to false personalization.
- If a previous call stored the wrong data, that mistake may repeat until corrected.
This is a general voice AI challenge: Data-driven personalization can misfire if the data is wrong — and AI won’t always know.
The Human Factor: Expectations vs Reality
We’ve noticed something interesting from early deployments:
Some users assume Phona is a traditional IVR (Interactive Voice Response) system. This leads to interactions like:
- “Sales.”
- “Billing.”
- “Operator.”
These single-word commands are fine in a keypad menu. But Phona isn’t a menu — it’s a conversational agent. It expects sentences like:
- “Hi, I’d like to speak to someone about a billing issue.”
That disconnect in expectation can lead to confusion. The good news? Most users adapt quickly — but it highlights the need for clear onboarding moments or agent intros that steer the caller toward conversational engagement.
Live Handoff Support (Coming Soon)
Currently, Phona does not support live call handoffs to human agents or other AI agents. This means it cannot yet:
- Transfer to a human mid-call
- Bridge to a separate department or AI instance
This is actively under development, especially for enterprise-grade deployments where escalation paths are essential. When complete, Phona will support dynamic routing decisions mid-conversation, putting it ahead of many competitors.
Determinism vs Naturalness
Not every Phona response is deterministic. That’s by design.
To avoid robotic, overly scripted interactions, Phona is allowed some creative variance in how it responds — especially in small talk, affirmations, and filler transitions.
But this comes with trade-offs:
- Too much variance → unpredictability
- Too little → monotony
We continuously tune this balance through prompt refinement, context weight adjustments, and guardrail layering. But enterprise users should be aware: Real-time AI voice is not a rules engine — it’s a probabilistic model.
What We’ve Done in Phona to Minimize and Overcome These Challenges
Phona was designed with contextual safeguards to reduce these limitations — though not eliminate them entirely.
- Contextual Prompts: Each Phona campaign is embedded with rich operational data — from hours of operation and refund policies to service workflows and FAQs. This gives the AI a factual baseline to ground its responses.
- Fallback Protocols: Phona uses call context to gracefully redirect when it’s unsure:
- “Let me check that with our team and get back to you,” or
- “I’ll make a note of that and escalate it right away.”
- Role-Aware Framing: Phona is trained to stay in character — not as an omniscient being, but as a helpful assistant working on behalf of the business. This reduces the likelihood of fabricated information slipping through.
Still, no AI — including Phona — can guarantee 100% precision in an open conversation. But by feeding in comprehensive brand data and carefully shaping the AI’s tone, scope, and fallback patterns, hallucinations and misfires can be minimized to manageable levels.
Guidelines for Enterprise Testers: Test Use-Cases, Not the AI
One of the most common mistakes in enterprise evaluations is adversarial testing — trying to “break” the AI instead of seeing how it performs in the actual use-case it’s meant to support.
While it’s natural to poke the edges, we recommend:
- Testing expected flows first: “Can it answer common queries?”
- Using realistic language: “How do real customers ask these questions?”
- Focusing on outcomes: “Did it book the appointment, qualify the lead, or escalate appropriately?”
Testing AI like a trick question game will always produce failures — because it’s designed for helpfulness, not perfection. But testing for business alignment will show its true value.
Final Thought: It’s Not About Perfection — It’s About Progress
AI voice technology is evolving rapidly. What seemed impossible three years ago — holding a fluid, natural-sounding conversation with a machine — is now routine. But real-time voice AI isn’t magic. It’s logic + probability + training data. And like any system built on those foundations, it has blind spots.
At Media Monk, we believe transparency matters. The future of AI voice will be built not by pretending it's perfect, but by building systems (like Phona) that acknowledge their limits and respond accordingly.
Want to see how Phona handles real-world conversations? Book a demo and explore the future of AI-powered communication — grounded in reality, not hype.
