# How to Handle Voicemail in AI Voice Agents

> Plura AI gives outbound voice agents built-in AMD, voicemail drop, and CRM logging so every unanswered call still drives results.

**Published:** 2026-09-02 | **Updated:** 2026-11-06 | **Author:** plurasync
**URL:** https://www.plura.ai/articles/handle-voicemail-ai-voice-agents
**Type:** post

**Categories:** AI Voice Agents

![How to Handle Voicemail in AI Voice Agents](https://plura.sites.aigrowthagent.co/wp-content/uploads/sites/75/2026/09/1788258148140-6cd5cdde064b-1024x572.webp)

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## Content

*Written by: Matt Beucler, CEO, Plura AI | Last updated: September 29, 2026*

## Key Voicemail Principles for AI Voice Agents

- Voicemail handling in AI voice agents splits into inbound, where the agent replaces voicemail, and outbound, where the agent relies on answering machine detection at the telephony layer.
- Outbound calls need AMD classification at the carrier layer with result values that drive branching actions and CRM disposition logging.
- Late-detection scenarios, where AMD returns a machine verdict after the AI starts speaking, require audio buffering, asynchronous AMD, or session teardown to deliver pre-generated voicemail messages cleanly.
- Voicemail messages should be pre-rendered with callback numbers stated twice and logged as first-class CRM dispositions separate from completed calls to drive accurate follow-up.
- Plura AI owns its FCC-licensed audio bridging carrier, so voicemail detection, outcome logging, and follow-up orchestration run as native platform capabilities.

[See how Plura AI handles voicemail detection.](https://www.plura.ai/plura-webchat?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)

## Outbound Voicemail Handling Steps for AI Voice Agents

The sequence below covers every state an outbound call can reach from dial to CRM disposition.

![Plura Predictive Dialer dashboard displaying AI-powered outbound call pacing, transfer analysis, and dialing performance insights.](https://cdn.aigrowthmarketer.co/1779338793506-2d33c5dff8e8.png)**Plura Predictive Dialer automates outbound calling with AI-powered pacing, transfer optimization, and real-time performance analytics.**

1. Dial the number and wait for answer detection.
2. Run AMD at the telephony layer, where the carrier can classify the audio before the LLM sees it.
3. Branch on the AMD result value: `human`, `machine_end_beep`, `machine_end_silence`, `unknown`, or `fax`.
4. If `human`, connect the [AI voice agent](https://plura.ai/ai-voice-demo?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent) to the live conversation immediately.
5. If `machine_end_beep` or `machine_end_silence`, wait for the signal and deliver the pre-generated voicemail message.
6. If `unknown`, apply a conservative fallback. Treat the call as human and connect the agent, or retry later.
7. If `fax`, hang up immediately and log the outcome as fax.
8. Log the call outcome in your CRM as a first-class disposition such as voicemail, not as a generic completed call.
9. Trigger follow-up automation based on the specific voicemail outcome.

## How Answering Machine Detection Behaves in AI Voice Agents

The steps above apply to outbound calls, and voicemail handling splits into two very different problems depending on direction. On inbound flows, the AI voice agent itself acts as the voicemail replacement. It answers, captures intent, and routes or logs the interaction, so detection is straightforward because the agent controls the session from the start.

Outbound flows introduce the AMD complexity. When an outbound call connects, the carrier voicemail system returns the same SIP response sequence as a live human. The sequence is provisional 100, 183 session progress, then 200 OK. The only signaling-level difference is time to answer, which is too noisy to classify reliably. Classification must happen in the media path, on the audio itself.

The outbound state machine has six states, and each one maps to a distinct branch. Misrouting at any state breaks the experience for the customer or the agent.

- **State 1: DIAL** – Outbound call placed.
- **State 2: ANSWER DETECTION** – Carrier signals answer.
- **State 3: AMD CLASSIFICATION** – Telephony layer classifies the answer as human, machine, fax, or unknown.
- **State 4a: HUMAN BRANCH** – Connect AI voice agent to the live conversation.
- **State 4b: MACHINE BRANCH** – Wait for beep or silence, then deliver the pre-generated voicemail.
- **State 4c: UNKNOWN BRANCH** – Apply a conservative fallback and treat the call as human or schedule a retry.
- **State 4d: FAX BRANCH** – Hang up and log the outcome as fax.
- **State 5: VOICEMAIL DELIVERY** – Play the pre-generated message after the beep or silence signal.
- **State 6: OUTCOME LOGGING** – Write the disposition to the CRM.

[Twilio’s AMD documentation](https://tessl.io/registry/skills/github/openai/plugins/twilio-voice-outbound-calls) describes two modes.4 `Enable` returns a verdict as soon as the system decides, with values `human`, `machine_start`, `fax`, and `unknown`. `DetectMessageEnd` waits for the greeting to finish before returning a result, with values `machine_end_beep`, `machine_end_silence`, `machine_end_other`, `human`, `fax`, and `unknown`. Use `DetectMessageEnd` when the goal is leaving a voicemail message.

[See AMD branching in a live call.](https://plura.ai/ai-voice-demo?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)

![Plura Predictive Dialer dashboard showing AI-powered outbound dialing, intelligent call routing, and performance analytics.](https://cdn.aigrowthmarketer.co/1779339309900-eefcb08741d1.png)**Plura Predictive Dialer uses AI-powered outbound dialing, intelligent routing, and real-time analytics to maximize call performance.**

## AMD Result Values and the Actions They Should Trigger

Every AMD result value maps to a specific action, and the list below shows what each one should trigger. Treating them as a binary human-or-machine flag creates most production misclassification problems, because each value requires a different response.

- **`human`** – Route the call to the AI voice agent immediately and start the conversation. This outcome carries the highest value and the highest cost when misclassified.
- **`machine_end_beep`** – The greeting has ended with a beep. Deliver the pre-generated voicemail message immediately.
- **`machine_end_silence`** – The greeting has ended with silence. Deliver the pre-generated voicemail message immediately. Some voicemail systems end with silence or verbal prompts, so this branch needs full support.
- **`unknown`** – The system ran out of time before reaching a confident classification. Because a false positive on a human is the costlier error, the conservative fallback is to treat the call as human and connect the agent, or to retry later. Do not guess machine and hang up. Treating uncertainty as human versus machine produces opposite failure signatures.
- **`fax`** – Hang up immediately and log the outcome as fax. Remove the number from the voice campaign and flag it for data verification.

Cekura’s AMD benchmark methodology recommends measuring four metrics separately because they move independently.4 Track false positive rate on humans, false negative rate on machines, time to verdict at p90 and p99, and behavior on an `unknown` verdict.

Twilio’s AMD accuracy is approximately [85 to 90%](https://tessl.io/registry/skills/github/openai/plugins/twilio-voice-outbound-calls).3 Implementations should expect some false positives and false negatives and tune the `machineDetectionSpeechThreshold` parameter, with a default of 2,400 ms, based on observed results. Organizations that systematically test and iteratively tune parameters like `speechThreshold` and `speechEndThreshold` often achieve meaningful accuracy improvements over default AMD settings, regardless of provider.

## Handling Voicemail Detected After the AI Starts Speaking

Late-detection is the failure mode that breaks production outbound campaigns at scale. It occurs when AMD returns a machine verdict after the AI voice agent has already started speaking. Three patterns address this scenario.

**Audio buffering.** Buffer the first few seconds of called-party audio so the AMD classifier has material to work with even after the AI has started speaking. Without the buffer, the classifier either gets a partial window or forces the call to sit in silence while it waits, which creates the dead-air problem described below.

**Running AMD asynchronously.** Asynchronous AMD connects the call immediately and lets a parallel classifier listen for the first second or two while the agent says something brief, then switches behavior based on the verdict. This approach avoids the dead air that synchronous AMD introduces, which real humans interpret as a robocall and hang up on. [Twilio’s asyncAmd=True parameter makes AMD non-blocking and delivers the result to the async_amd_status_callback webhook.](https://tessl.io/registry/skills/github/openai/plugins/twilio-voice-outbound-calls)

**Tearing down the AI session if a machine is detected mid-conversation.** If AMD returns a machine verdict after the AI has already started speaking, tear down the conversational AI session. Switch to the pre-generated voicemail message and deliver it after the beep. Do not let the LLM continue improvising into a recording. [Voicemail systems expect a continuous audio stream, so streaming TTS can be truncated mid-message by sub-second gaps that look like end-of-message silence.](https://channel.tel/blog/voicemail-drop-callback-agent) Pre-render the message, store it, and play the file.

**Design the opener to work for either audience.** An opener such as “Hi, this is [Name]” sounds normal to a human and gives the classifier room to commit before the agent says anything irreversible. The opener is the single most important design decision in an async AMD deployment.

[See late-detection handling in action.](https://plura.ai/ai-voice-demo?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)

## Voicemail Scripts That Work for AI Voice Agents

The voicemail message is generated before the call and stored as a file, so the conversational agent never improvises it. Pre-render it once, store it, and play the file after the beep. The following template covers the required elements.

“Hi [First Name], this is [Agent Name] calling from [Company Name]. I’m reaching out about [specific reason, e.g., your recent inquiry about X]. The best number to reach me is [callback number], that’s [repeat callback number]. I’ll also send a quick text so you have it. Thanks, and I look forward to connecting.”

Keep the message between 15 and 20 seconds, roughly 60 to 80 words at a natural speaking pace. [State the callback number twice, once early and once at the end, so a message clipped at either edge still delivers the callback information.](https://vixi.agency/blog/ai-voice-agent-voicemail-detection) Many voicemail systems play a long greeting before the beep, and carriers often truncate recordings past 30 seconds.

Streaming TTS introduces sub-second gaps that some voicemail systems interpret as end-of-message silence, truncating the recording mid-sentence. Pre-rendering eliminates this failure mode entirely. [A voicemail script with three personalization variables, such as name, company pain, and callback number, hits the personalization bar without the latency cost of full LLM generation per call and can be synthesized once during dial and cached by lead ID.](https://channel.tel/blog/voicemail-drop-callback-agent)

## Logging Voicemail Outcomes in Your CRM

Collapsing voicemail into a generic completed outcome breaks follow-up automation. The system cannot distinguish a live conversation from a machine pickup, so the follow-up trigger fires incorrectly. The correct taxonomy treats each outcome as a first-class disposition with its own follow-up trigger. Plura’s [CRM integration](https://plura.ai/integrations?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent) writes these dispositions back to your system of record automatically.

- **answered** – Live human conversation occurred. Trigger post-call follow-up based on conversation content.
- **voicemail** – Machine answered and a message was delivered. Trigger a voicemail-specific follow-up cadence such as SMS or email with the same information.
- **no_answer** – Call rang out. Schedule a retry per cadence rules.
- **busy** – Line was busy. Retry after a short interval.
- **failed** – Call could not be placed. Investigate number validity.
- **fax** – Fax tone detected. Remove from the voice campaign and flag for data verification.
- **unknown** – Classification timed out. Apply a conservative fallback and log the call for review.
- **callback_requested** – Prospect requested a callback. Route the lead to a priority queue.

Call detail records capture the outcome of each dial, and a wall of two-second “answers” usually indicates voicemail pickups masquerading as connects. [Whether voicemails count as connects depends on how a dialer or CRM defines each call disposition](https://calllogic.com/blog/connect-rate-vs-contact-rate-whats-the-difference), which is why documented disposition logic is a prerequisite for benchmarking. Plura’s Stateful Conversation Database holds context across every channel, so a voicemail left on Monday informs the SMS follow-up on Tuesday.

![Plura Conversation Intelligence dashboard displaying AI-powered call analytics, transfer tracking, and customer conversation insights.](https://cdn.aigrowthmarketer.co/1779338480670-5b2fbc1c92ba.png)**Plura Conversation Intelligence gives businesses AI-powered analytics, call transfer tracking, and customer interaction insights across every conversation.**

## Test Matrix for Voicemail Detection in AI Voice Campaigns

AMD regresses on every change to the voicemail classifier prompt, every LLM version bump, every TTS provider swap, and every silence-threshold tune. [Any code or config change that modifies AMD behavior should ship behind a green test suite.](https://roark.ai/blog/testing-voicemail-detection-outbound-voice-agents) The scenarios below fall into four failure classes: greeting length, voice identity, call screening, and post-start detection.

- **Short greetings (under 3 seconds)** – Twilio’s documentation gives the example that AMD “may interpret a very short two-second voicemail greeting as a human picking up.”4 Verify the classifier catches short voicemail greetings before they reach the agent.
- **Long greetings (15 seconds or more)** – Verify the agent waits for the beep and does not start speaking mid-greeting. The AMD `beepMaxAwaitSeconds` parameter controls how long the system waits for the voicemail beep before speaking, and values under 15 seconds risk the message being cut off.
- **No-beep greetings** – Some voicemail systems end with silence. Verify the agent delivers the message after silence via the `machine_end_silence` branch.
- **Human-sounding machines** – Custom greetings recorded by the same person who would answer live. Verify the classifier distinguishes based on cadence and content rather than voice identity.
- **Machine-sounding humans** – Slow or non-native speakers with long greetings. Verify the classifier does not false-positive on a human. [A false positive on humans in async AMD can cause the agent to hang up on a live lead or launch into a pre-recorded voicemail script on a live person.](https://roark.ai/blog/testing-voicemail-detection-outbound-voice-agents)
- **Call screening (iOS 26, Google Pixel)** – [Screeners ask the caller to state their purpose.](https://roark.ai/blog/testing-voicemail-detection-outbound-voice-agents) Verify the agent briefly states its purpose, honors the screener timeout, and hangs up cleanly.
- **Post-start detection** – Verify that if AMD returns a machine verdict after the AI has started speaking, the session tears down and the voicemail message is delivered correctly without clipping the first seconds.

## Voicemail Message Compliance Considerations

Testing covers the technical failure modes, but outbound voicemail also carries a regulatory dimension that operators should review with qualified counsel. The framework below is described neutrally and is not legal advice.

Under 47 CFR 64.1200, prerecorded or artificial voice telemarketing messages must state the name of the business placing the call and include a telephone number where the business can be reached.2 [Prerecorded telemarketing messages must also include an automated, interactive opt-out mechanism available throughout the message.](https://leadcompliant.com/articles/tcpa-basics/what-is-an-opt-out-mechanism-for-telemarketing-calls) [Telemarketing calls to residential subscribers are restricted to specific hours in the called party’s local time.](https://tcn.com/en_gb/complete-guide-to-tcpa-compliance)2 [The FCC ruled in February 2024 that AI-generated voices fall under the TCPA’s artificial or prerecorded voice provisions.](https://leadcompliant.com/articles/cold-calling-rules/required-disclosures-at-beginning-of-outbound-telemarketing-call)2 Operators should consult the regulation and qualified counsel to understand how these frameworks apply to their specific campaigns.

Plura supports TCPA compliance and DNC compliance at the platform level, with real-time DNC scrubbing applied to every outbound contact before dial and immutable consent logging built into the platform.1 Customers remain responsible for their own regulatory obligations and the claims they make to their end users.

![Screenshot of Plura’s fully compliant AI communications platform showing business registration and phone number provisioning workflows for AI Voice, SMS, RCS, and Webchat communication automation.](https://cdn.aigrowthmarketer.co/1779337911454-8c3a9645d906.png)**Plura’s FCC-licensed AI communications platform simplifies compliant business registration and phone number provisioning for AI Voice, SMS, RCS, and Webchat workflows.**

## Why Plura AI Is Built for Voicemail Handling in AI Voice Agents

Compliance and detection both depend on where the telephony layer sits. Plura AI owns its FCC-licensed audio bridging carrier. That ownership means [voicemail detection](https://plura.ai/ai-predictive-dialer?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent) and outcome handling run at the telephony layer inside the platform, not through third-party APIs.

- Plura AI owns its FCC-licensed audio bridging carrier. AMD result values are mapped to specific actions and outcome logging is built in, instead of being assembled from external webhooks.
- The [AI Predictive Dialer](https://plura.ai/ai-predictive-dialer?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent) includes voicemail detection as a first-class capability, with branching logic for every AMD result value and CRM disposition writing handled inside the platform.
- Voicemail is logged as a first-class call outcome, so follow-up automation triggers correctly and does not treat a machine pickup as a completed conversation.
- The Stateful Conversation Database described earlier keeps that context across channels, so the Monday voicemail still informs Tuesday’s SMS follow-up.
- Branded caller ID is issued at the carrier level, which helps calls present with the company’s name rather than “Spam Likely” before AMD even runs.

For operators evaluating [plans and rates](https://plura.ai/pricing?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent), Plura’s architecture means voicemail detection, outcome logging, and follow-up orchestration function as platform capabilities rather than integration projects. See how the numbers stack up with Plura’s [ROI calculator](https://plura.ai/calculator?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent).

## Frequently Asked Questions

### Can an AI Agent Leave a Voicemail?

An AI voice agent can leave a voicemail by waiting for the beep or silence signal after AMD returns a machine verdict, then playing a pre-generated message. The message should be pre-rendered before the call and not generated in real time by the conversational agent, which avoids truncation from streaming TTS gaps.

### How Do You Set Up Voicemail Detection for an Outbound AI Agent?

Configure AMD at the telephony layer using a mode that waits for the greeting to finish, such as Twilio’s `DetectMessageEnd` mode. Map each result value to a specific action. Connect the agent on `human`, deliver the pre-generated message on `machine_end_beep` and `machine_end_silence`, apply the conservative fallback on `unknown`, and hang up on `fax`. Pre-generate the voicemail message before the call and log voicemail as a first-class CRM disposition with its own follow-up trigger.

### What Are AMD False Positives and Why Do They Matter?

A false positive occurs when a real person is classified as a machine, which causes the agent to hang up or leave a pre-recorded message on a live human. This error is costlier in outbound campaigns because it removes the conversation opportunity entirely. False negatives, where the system treats a machine as human, waste agent time but preserve the number for a retry. Track false positives and false negatives separately because they move independently and require different tuning responses.

### How Does Voicemail Greeting Length Affect Detection Accuracy?

Voicemail greetings are long uninterrupted stretches of speech, typically several seconds, while a human usually says a short phrase and stops. Short greetings under 3 seconds risk misclassification as human because the classifier does not have enough audio to commit. Long greetings require the agent to wait for the beep and avoid speaking mid-greeting. Raising the speech threshold reduces false machine detections on verbose human greetings but increases detection time on every call.

### How Do I Test Voicemail Detection in Production?

Build a test matrix covering short greetings, long greetings, no-beep greetings that end with silence, human-sounding machines with custom greetings, machine-sounding humans with slow cadence, iOS and Google Pixel call screening, and post-start detection where AMD returns a verdict after the agent has already begun speaking. Test over real telephony rather than transcripts, because codec artifacts and packet loss are what break classifiers in production.

[Review your voicemail test plan with Plura.](https://plura.ai/ai-voice-demo?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)

1 Plura AI maintains SOC 2, HIPAA, ISO, and GDPR posture as part of its platform infrastructure. References to compliance frameworks in this article describe Plura’s platform capabilities and do not constitute a guarantee that any customer using Plura will themselves be compliant with applicable laws or standards. Customers remain solely responsible for their own regulatory obligations, certifications, consent management, recordkeeping, and the claims they make to their own end users. Consult qualified legal counsel for guidance specific to your use case.

2 This article describes regulatory frameworks at a general level and does not constitute legal advice. Laws and regulations vary by jurisdiction, change over time, and apply differently depending on facts and circumstances. Readers should consult qualified legal counsel before making compliance decisions.

3 Performance figures, customer outcomes, and industry statistics referenced in this article are drawn from cited third-party sources or Plura customer case studies. Individual results vary based on implementation, use case, industry, audience, and execution. Past or aggregate performance is not a guarantee of future results.

4 References to third-party products, services, companies, or research are made for informational and comparative purposes only. Plura AI is not affiliated with, endorsed by, or sponsored by any third party named in this article unless explicitly stated. Trademarks and product names referenced remain the property of their respective owners.

This article is provided for informational purposes only and reflects Plura AI’s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.

This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.

## Read Next

- [Voicemail Detection for Outbound AI Agents Explained](https://www.plura.ai/articles/voicemail-detection-voice-ai/?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)
- [Voicemail Detection Best Practices: An Operator’s Playbook](https://www.plura.ai/articles/best-voicemail-detection-practices/?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)
- [How Accurate Is Voicemail Detection? A Guide to AMD](https://www.plura.ai/articles/how-accurate-is-voicemail-detection/?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)
- [Voicemail Detection for Call Centers: AMD Accuracy Guide](https://www.plura.ai/articles/best-voicemail-detection-call-center/?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)
- [Voicemail Detection Software: How AMD Works in 2026](https://www.plura.ai/articles/best-voicemail-detection-software/?utm_term=handle-voicemail-ai-voice-agents&utm_campaign=ai_growth_agent)

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---

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- **OpenAI Plugin Manifest:** [https://www.plura.ai/articles/.well-known/ai-plugin.json](https://www.plura.ai/articles/.well-known/ai-plugin.json)
- **A2A Agent Card:** [https://www.plura.ai/articles/.well-known/agent-card.json](https://www.plura.ai/articles/.well-known/agent-card.json)
- **MCP Server (Streamable HTTP):** [https://www.plura.ai/articles/.well-known/mcp](https://www.plura.ai/articles/.well-known/mcp)

## Citations

- [AI Voice Agents for Multiple Clients: The Agency Playbook](https://www.plura.ai/articles/ai-voice-agent-multiple-clients)
- [Best AI Voice Agent for Window and Door Replacement Leads](https://www.plura.ai/articles/best-ai-voice-agent-window-door)
- [AI Voice Agent Opt-Out Compliance: 2026 Rules](https://www.plura.ai/articles/voice-agent-opt-out-compliance)
- [AI Voice Agent With Warm Transfer for Lead Follow-Up](https://www.plura.ai/articles/ai-voice-agent-warm-transfer)
- [Voice AI for HVAC Companies: The 2026 Buyer’s Guide](https://www.plura.ai/articles/best-voice-ai-hvac-companies)

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