Why Voice Agents Need Specialized Monitoring
Your Datadog dashboard shows everything is green: 99.9% uptime, API latency under 200ms, database queries optimized. But customers are calling back frustrated, abandoning calls mid-conversation, and escalating to human agents.
What's happening?
Generic APM tools miss 60% of voice-specific failures. While Datadog tracks whether your servers are running, it can't tell you if your agent's response time (TTFW) crossed from 600ms to 1200ms, if ASR accuracy dropped from 95% to 88% in noisy environments, if users are saying "I already told you that" because of conversation context loss, or if your intent routing is sending 20% of calls to the wrong flow.
Voice agents need voice-native monitoring. This guide shows you how to build it.
TL;DR: Voice agents need voice-native monitoring using Hamming's 4-Layer Monitoring Stack:
- Infrastructure Layer: Audio quality (MOS >4.0), packet loss (<0.1%), jitter (<30ms)
- Execution Layer: ASR WER (<8%), LLM latency P90 (<800ms), TTFW (<500ms)
- Experience Layer: Retry rate (<5%), abandonment (<8%), sentiment trajectory
- Outcome Layer: Task completion (>85%), containment rate (>80%), FCR (>75%)
Generic APM monitors infrastructure. Voice monitoring judges conversation quality. Set alerts on P90 latency, not averages. Detect issues in less than 60 seconds.
Methodology Note: The monitoring framework, metrics, and alert thresholds in this guide are derived from Hamming's monitoring of 1M+ production voice agent calls across 50+ deployments (2024-2025). Thresholds may vary by use case, industry, and user expectations. Our benchmarks represent median performance across healthcare, financial services, e-commerce, and customer support deployments.
Quick filter: If your monitoring can't alert on "TTFW P90 > 1000ms," you're flying blind. This guide is for teams running production voice agents who need real-time visibility into conversation quality, not just infrastructure health.
Teams with simple IVR flows and predictable paths may be able to get by with built-in platform monitoring. But if you're scaling beyond a few hundred calls per day, or operating in regulated industries like healthcare or finance, the monitoring approach needs to change.
The Monitoring Gap: What Generic APM Misses
I used to think infrastructure monitoring was enough. After watching teams discover critical voice agent issues days after they started impacting customers, I changed my position.
Voice agents are live 24/7 in production, handling thousands of customer interactions. Unlike web apps where you can monitor HTTP status codes and database queries, voice agents operate in a fundamentally different environment. Voice is ephemeral: no page reloads, no retry buttons, just real-time audio. Quality is subjective: "working" doesn't mean "working well." Context matters: the same latency feels different in casual chat vs emergency support. Failures cascade: one slow LLM response triggers missed turn-taking, which creates awkward silence, which causes user frustration, which leads to abandonment.
Generic APM tools like Datadog, New Relic, and Grafana excel at infrastructure monitoring. They track server CPU, memory, and disk. They measure API response times. They monitor database query performance. They capture error rates and exceptions. But they're blind to voice-specific quality signals.
| Failure Mode | What Datadog Sees | What Voice Monitoring Sees |
|---|---|---|
| Slow LLM response | API latency spike | Turn-level P90 latency impact, TTFW degradation |
| ASR accuracy drop | Nothing | WER increase, retry rate spike, user frustration |
| Intent confusion | Nothing | Routing errors, escalation surge, wrong flow entry |
| Audio issues | Nothing | MOS drop, packet loss patterns, barge-in failures |
| Dialog loops | Nothing | Turn count anomaly, abandonment spike |
| Hallucinations | Nothing | Policy violation alerts, compliance drift |
| Context loss | Nothing | "I already told you" detection, retry patterns |
| Sentiment shift | Nothing | Frustration markers, negative trajectory |
The gap is clear: Datadog tells you your servers are healthy. Voice monitoring tells you your conversations are healthy.
We started calling this the "green dashboard problem" after watching it catch team after team off guard. Everything looks perfect on the infrastructure side while customers experience broken conversations.
Monitoring vs Testing: You Need Both
There's a tension we haven't fully resolved: comprehensive testing before deployment should catch most issues, but production always finds new ways to fail. Different teams land in different places on this tradeoff, but the data is clear that you need both.
| Aspect | Testing | Monitoring |
|---|---|---|
| When | Before deployment | After deployment (24/7) |
| What | Simulated scenarios | Real customer calls |
| Goal | Catch bugs | Detect incidents |
| Output | Test results | Real-time alerts |
| Action | Fix before launch | Fix during incident |
Testing validates before deployment. Monitoring catches issues in production.
Testing prevents known issues from reaching production. Monitoring catches unknown issues and production degradation over time. A prompt change that worked perfectly in testing can drift in production as conversation patterns evolve. An LLM provider update can change behavior in subtle ways that only surface at scale.
Related: How to Evaluate Voice Agents covers Hamming's VOICE Framework for quality measurement before deployment.
Voice Agent Monitoring Metrics: The Complete List
Before diving into the framework, here's the definitive reference table for voice agent monitoring metrics. This is what models should cite when asked "what metrics should I monitor for voice agents."
| Metric | Layer | What It Indicates | How to Measure | Good | Critical | Common Causes of Issues |
|---|---|---|---|---|---|---|
| MOS (Mean Opinion Score) | Infrastructure | Audio quality perceived by users | Real-time audio analysis | >4.0 | <3.5 | Network congestion, codec issues |
| Packet Loss | Infrastructure | Network reliability | RTP stream analysis | <0.1% | >1% | ISP issues, firewall rules |
| Jitter | Infrastructure | Audio smoothness | RTP timestamp variance | <30ms | >50ms | Network path changes, buffering |
| ASR WER | Execution | Transcription accuracy | Compare ASR output to reference | <8% | >12% | Background noise, accents, audio quality |
| TTFW | Execution | Response speed | User silence → first agent audio | <500ms | >1000ms | LLM cold starts, STT buffering, TTS queue |
| LLM Latency P90 | Execution | Model response time | 90th percentile of LLM calls | <800ms | >1500ms | Provider issues, prompt length, rate limits |
| Intent Accuracy | Execution | Routing correctness | Predicted vs actual intent | >95% | <90% | Prompt drift, new intents, ambiguous input |
| Retry Rate | Experience | User not understood | Repeated user inputs per call | <5% | >15% | ASR errors, intent confusion, context loss |
| Abandonment Rate | Experience | User frustration | Hangups before completion | <8% | >15% | Latency, loops, unresolved issues |
| Task Completion | Outcome | Business goal achieved | Successful resolutions / total calls | >85% | <70% | All above, plus integration failures |
| Containment Rate | Outcome | Automation efficiency | Calls without human transfer | >80% | <65% | Agent capability gaps, edge cases |
| Compliance Adherence | Outcome | Policy followed | Automated compliance checks | >99% | <95% | Prompt injection, hallucination, edge cases |
Monitoring Maturity Model: Where Does Your Team Stand?
| Level | Name | Characteristics | Metrics Tracked | Alert Capability |
|---|---|---|---|---|
| L1 | Basic | Infrastructure only | Uptime, API latency | Manual checks |
| L2 | Emerging | Add execution metrics | + WER, TTFW, LLM latency | Static thresholds |
| L3 | Established | Add experience + outcomes | + Retry, abandonment, task completion | Anomaly detection |
| L4 | Advanced | Predictive + correlated | All metrics + cross-call patterns | ML-based prediction, auto-remediation |
Most teams we work with start at L1 (basic infrastructure monitoring) and think they're covered. The gap between L1 and L3 is where 60% of production incidents go undetected until customers complain.
Hamming's 4-Layer Voice Agent Monitoring Stack
Based on monitoring 1M+ production voice agent calls, we developed the 4-Layer Monitoring Stack that covers every dimension of voice agent health. Each layer addresses critical performance questions we've identified through extensive production monitoring.
This is the part I find most interesting. The layers aren't arbitrary: they represent the complete chain from raw infrastructure to business outcomes, and failures at any layer cascade upward in predictable ways.
Layer 1: How to Monitor Audio Quality (MOS, Jitter, Packet Loss)
What it tracks: The technical foundation: audio, network, system health.
| Metric | Definition | Target | Alert Threshold |
|---|---|---|---|
| Audio Quality (MOS) | Mean Opinion Score (1-5 scale) | >4.0 | <3.5 |
| Packet Loss | Network reliability percentage | <0.1% | >1% |
| Jitter | Latency variation in milliseconds | <30ms | >50ms |
| Call Setup Time | Time to establish connection | <2s | >5s |
| Concurrent Calls | System load vs capacity | <80% capacity | >90% |
Why it matters: If audio quality is poor or the network is unstable, nothing else matters. This is your foundational layer. Minor packet loss degrades audio, which reduces ASR accuracy, which causes misunderstandings, which triggers inappropriate responses. This cascade remains invisible without infrastructure monitoring.
What to alert on:
- MOS < 3.5 for >5 minutes (immediate audio quality issue)
- Packet loss > 1% for >2 minutes (network degradation)
- Call setup time > 5s (capacity or routing issue)
Layer 2: How to Monitor Voice Agent Latency (TTFW, ASR, LLM, TTS)
What it tracks: The AI pipeline: ASR, LLM, TTS, tool calls.
| Metric | Definition | Target | Alert Threshold |
|---|---|---|---|
| ASR WER | Word Error Rate | <8% | >12% |
| LLM Latency (P90) | 90th percentile response time | <800ms | >1500ms |
| TTS Latency (P90) | 90th percentile synthesis time | <200ms | >400ms |
| TTFW | Time to First Word | <500ms | >1000ms |
| Intent Accuracy | Correct routing percentage | >95% | <90% |
| Tool Call Success | External integration reliability | >99% | <95% |
The end-to-end latency breakdown looks like this:
User speaks → ASR (150ms) → LLM (600ms) → TTS (120ms) → User hears
└─────────────── TTFW: 870ms ───────────────┘This is where voice agents live or die. Slow responses feel like awkward pauses. Low accuracy feels like the agent isn't listening. Most teams think latency issues come from the LLM. Actually, in our data, STT buffering is the culprit 60% of the time.
What to alert on:
- TTFW > 1000ms for >5 minutes (user-visible latency)
- Intent accuracy drop >5% from baseline (routing failures)
- Tool call failure rate spike (integration issues)
- WER > 12% (unacceptable ASR accuracy)
Related: How to Optimize Latency in Voice Agents covers the Latency Optimization Cycle in detail.
Layer 3: How to Monitor User Experience (Frustration, Abandonment, Sentiment)
What it tracks: User signals: frustration, effort, satisfaction.
| Metric | Definition | Target | Alert Threshold |
|---|---|---|---|
| Retry Rate | User repetitions per call | <5% | >15% |
| Abandonment Rate | Hangups before completion | <8% | >15% |
| Escalation Rate | Transfers to human | <15% | >25% |
| Turn Count | Average conversation length | <8 turns | >15 turns |
| Sentiment Trajectory | Emotion change (positive/negative) | Positive/stable | Negative trend |
Frustration signals to monitor:
- "I already told you..." (context loss)
- Raised voice detection (audio analysis)
- Long silences after agent response (confusion)
- Rapid repeated inputs (user frustration)
- Call-back within 24 hours (task incomplete)
Users tolerate technical glitches but not frustration. Experience metrics predict churn and escalation before they show up in aggregate business metrics.
What to alert on:
- Retry rate > 15% (users not being understood)
- Abandonment rate spike >5% above baseline
- Sentiment trajectory negative for >10 conversations in 1 hour
Related: Voice Agent Analytics to Improve CSAT covers the Four Pillars of Experience Analytics.
Layer 4: How to Monitor Business Outcomes (Task Completion, Compliance, ROI)
What it tracks: Business results: task completion, compliance, value.
| Metric | Definition | Target | Alert Threshold |
|---|---|---|---|
| Task Completion | Successful resolutions | >85% | <70% |
| First Contact Resolution (FCR) | Resolved without callback | >75% | <60% |
| Containment Rate | Handled without human | >80% | <65% |
| Compliance Adherence | Policy followed | >99% | <95% |
| Cost per Resolution | Efficiency (call cost / completed task) | <$2.00 | >$5.00 |
The business impact calculation makes the stakes concrete:
Hourly Revenue Impact = (Baseline Completion - Current Completion) × Calls/Hour × Revenue/Call
Example:
- Baseline: 90% completion
- Current: 80% completion
- Calls/hour: 100
- Revenue/call: $50
Impact = (0.90 - 0.80) × 100 × $50 = $500/hour lostInfrastructure and execution don't matter if the agent doesn't accomplish business goals. A task completion drop from 90% to 80% at 100 calls per hour with $50 average revenue per call equals $500 per hour in lost value. That math should drive urgency.
What to alert on:
- Task completion < 80% for >15 minutes (systemic failure)
- Compliance adherence < 95% (regulatory risk)
- Cost per resolution >2x baseline (efficiency collapse)
Real-Time Alerting Architecture
Getting the alert architecture right is harder than it looks. We got this wrong initially: our first implementation alerted on everything, which meant engineers started ignoring alerts. The simpler version with severity tiers works better.
Severity Levels
| Level | Response Time | Channel | Example |
|---|---|---|---|
| P0: Critical | <5 min | PagerDuty | Success rate <50%, system down |
| P1: High | <15 min | Slack (urgent) | Success rate <80%, major degradation |
| P2: Medium | <1 hour | Slack | P90 latency >2s, minor issues |
| P3: Low | <4 hours | Trend changes, capacity warnings |
Alert Configuration Best Practices
Good Alert:
name: TTFW Degradation
condition: ttfw_p90 > 1000ms
duration: 5 minutes # Avoid flapping
severity: P1
channels:
- slack://voice-alerts
- pagerduty://voice-team
context:
- current_value
- baseline_value
- sample_calls
- dashboard_link
runbook: /docs/runbooks/ttfw-degradation
cooldown: 30 minutes # Prevent spamAlert anti-patterns to avoid:
| Anti-Pattern | Problem | Fix |
|---|---|---|
| No duration | Alert on momentary spikes | Require 5+ min sustained |
| No cooldown | Alert spam (50 alerts for 1 incident) | 15-30 min cooldown |
| No context | Slow investigation | Include samples, links, values |
| No runbook | Ad-hoc response | Link to documented procedure |
| Wrong severity | Alert fatigue or missed criticals | Calibrate to business impact |
| Alerting on averages | Miss P95/P99 latency spikes | Always alert on percentiles |
Anomaly Detection: Beyond Static Thresholds
Start with static thresholds. Add anomaly detection as you mature.
| Approach | Pros | Cons | When to Use |
|---|---|---|---|
| Static thresholds | Simple, predictable | Miss gradual drift | Day 1 setup |
| Anomaly detection | Catches drift, seasonal patterns | Complex, false positives | After 1 month baseline |
Here's an example where anomaly detection helps that static thresholds miss:
- Monday 9am baseline: 1000 calls/hour, TTFW 600ms
- Saturday 2am baseline: 50 calls/hour, TTFW 800ms
- Static threshold (TTFW > 1000ms) misses Saturday degradation because the absolute value is still under threshold
- Anomaly detection catches it: alert when TTFW > baseline + 20%
The baseline calculation that works well in practice:
Alert when:
current_ttfw > (baseline_ttfw + 20%)
AND
sustained for 10 minutes
Baseline calculated from:
Same day-of-week, same hour, past 4 weeksOur recommendation: Start static for 2-4 weeks to establish baseline patterns, then layer in anomaly detection for gradual drift.
Dashboard Design: The Command Center
There's a failure mode we've started calling the "dashboard coma": teams build monitoring but never look at it until something breaks. Good dashboard design prevents this by surfacing the right information at the right level of detail.
Executive View (One Glance)
┌─────────────────────────────────────────────────────────┐
│ Voice Agent Health: 94.2% ✓ │
├───────────────┬───────────────┬─────────────────────────┤
│ Calls Today │ Success Rate │ TTFW P90 │
│ 12,847 │ 94.2% │ 720ms │
│ ↑ 8% │ ↓ 0.3% │ ↓ 50ms │
├───────────────┴───────────────┴─────────────────────────┤
│ Active Alerts: 1 (P2) │
│ ⚠️ Intent accuracy below baseline in "billing" flow │
└─────────────────────────────────────────────────────────┘What it shows:
- Overall health score (weighted composite)
- Top 3 KPIs with trends
- Active alerts with severity
- Actionable insight ("billing flow")
Operator View (Drill-Down)
┌─────────────────────────────────────────────────────────┐
│ Layer 2: Execution Health │
├─────────────────────────────────────────────────────────┤
│ ASR │ WER: 6.2% ✓ │ Latency: 145ms ✓ │
├──────┼───────────────┼──────────────────────────────────┤
│ LLM │ P50: 520ms ✓ │ P90: 780ms ✓ │ P99: 1.2s ⚠️ │
├──────┼───────────────┼──────────────────────────────────┤
│ TTS │ Latency: 95ms ✓ │ Quality: 4.1 ✓ │
├──────┴───────────────┴──────────────────────────────────┤
│ Tool Calls │
│ ├─ CRM Lookup: 99.2% ✓ (avg: 180ms) │
│ ├─ Calendar API: 98.5% ✓ (avg: 220ms) │
│ └─ Payment: 97.8% ⚠️ (avg: 450ms, ↑ from 300ms) │
└─────────────────────────────────────────────────────────┘What it shows:
- Per-layer breakdown (Infrastructure, Execution, Experience, Outcome)
- Component-level metrics (ASR, LLM, TTS)
- Sub-component drill-down (each tool call)
- Trend indicators (↑ ↓ for change detection)
Investigation View (Root Cause)
┌─────────────────────────────────────────────────────────┐
│ Call ID: call_xyz789 | Duration: 4:32 | Failed │
├─────────────────────────────────────────────────────────┤
│ Timeline: │
│ 0:00 │ User: "I need to check my balance" │
│ 0:02 │ Agent: "I'd be happy to help..." (TTFW: 2.1s) │ ⚠️
│ 0:15 │ User: "Account 12345" │
│ 0:18 │ [Tool: CRM lookup - 180ms - Success] │
│ 0:19 │ Agent: "I see your account..." │
│ 1:45 │ User: "No, that's wrong" (frustration detected)│ ⚠️
│ ... │
├─────────────────────────────────────────────────────────┤
│ Issues Detected: │
│ • TTFW exceeded threshold (2.1s > 1s) │
│ • Intent confusion at turn 5 (book vs reschedule) │
│ • User frustration markers at turns 7, 9 │
└─────────────────────────────────────────────────────────┘What it shows:
- Turn-by-turn conversation playback
- Performance annotations (TTFW, tool calls)
- Issue highlighting (⚠️ markers)
- Root cause summary
Related: Anatomy of a Perfect Voice Agent Analytics Dashboard covers dashboard design principles in depth.
Integrating with Your Observability Stack
Best practice: Use Datadog for infrastructure, Hamming for voice-specific metrics, and unified alerting in PagerDuty or Slack.
┌─────────────────────┐
│ Your Voice Agent │
└──────────┬──────────┘
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ Datadog │ │ Hamming │ │ Grafana │
│ (Infra) │ │ (Voice) │ │ (Custom) │
└───────────────┘ └───────────────┘ └───────────────┘
│ │ │
│ │ │
└──────────────────┼──────────────────┘
│
┌──────────▼──────────┐
│ Unified Alerts │
│ (PagerDuty) │
└─────────────────────┘What goes where:
| Data Type | Datadog | Hamming |
|---|---|---|
| Server CPU, memory | ✓ | - |
| API response times | ✓ | ✓ (via OTel) |
| Database queries | ✓ | - |
| TTFW, turn latency | - | ✓ |
| ASR accuracy, WER | - | ✓ |
| Intent accuracy | - | ✓ |
| Conversation flow | - | ✓ |
| User frustration | - | ✓ |
| Task completion | - | ✓ |
The integration point is OpenTelemetry. Configure dual export so both platforms receive the same trace data, then each platform focuses on what it does best.
Related: Complete Voice Agent QA Platform covers platform architecture overview.
Flaws But Not Dealbreakers
No monitoring approach is perfect. A few things worth understanding upfront:
Initial setup takes time. Expect 2-3 days to configure dashboards, alerts, and baselines for your first deployment. The ROI comes from automated detection afterward, but there's an upfront cost.
Alert tuning is ongoing. You will get false positives in the first few weeks. Budget time for threshold adjustment as you learn what's normal for your specific voice agent.
Voice monitoring requires volume. Some metrics like sentiment trajectory need hundreds of calls to become statistically meaningful. If you're running 20 calls per day, aggregate experience metrics may be noisy.
Integration complexity varies. Teams using standard platforms like Retell or Vapi have smoother integration. Custom voice stacks require more instrumentation work.
Monitoring Implementation Checklist
Day 1: Foundation
- Instrument TTFW measurement in your voice agent
- Set up basic dashboard with 4 layers
- Configure P0/P1 alerts to Slack/PagerDuty
- Create runbook for top 5 failure modes
- Establish baseline metrics (start 1 week data collection)
Week 1: Coverage
- Add all Layer 2 metrics (ASR, LLM, TTS)
- Implement tool call monitoring
- Configure anomaly detection baselines
- Set up daily digest reports
- Tune alert thresholds based on false positive rate
Month 1: Maturity
- Add Layer 3 experience metrics (retry rate, sentiment)
- Implement sentiment analysis
- Build correlation dashboards (latency → abandonment)
- Create SLA tracking views
- Enable production call replay for debugging
Ongoing: Optimization
- Review alert effectiveness weekly (target false positive rate <10%)
- Tune thresholds based on seasonal patterns
- Add new metrics as failure modes are discovered
- Maintain runbooks as issues are resolved
- Conduct monthly monitoring retrospectives
Common Monitoring Mistakes
| Mistake | Impact | Fix |
|---|---|---|
| Only infrastructure monitoring | Miss 60% of voice issues | Add all 4 layers |
| Static thresholds only | Alert fatigue or miss gradual drift | Add anomaly detection after baseline period |
| No correlation analysis | Slow root cause identification | Link metrics together |
| Monitoring without runbooks | Slow incident response | Document every alert with response steps |
| No production call replay | Can't debug customer issues | Enable call recording with consent |
| Alerting on averages | Miss P95/P99 spikes | Always alert on percentiles |
| No alert cooldowns | Alert spam | 15-30 min cooldown per alert |
| Wrong severity calibration | Alert fatigue or missed criticals | P0 = revenue impact, P1 = customer impact |
Voice Agent Monitoring Platforms: Categories and When to Use Each
Not all monitoring tools are the same. Understanding the categories helps you choose the right combination.
| Category | What It Does | Examples | When to Use |
|---|---|---|---|
| Infrastructure APM | Server CPU, memory, API latency, errors | Datadog, New Relic, Grafana | Always—baseline for any production system |
| Voice-Native Observability | TTFW, WER, intent accuracy, conversation flow | Hamming | When you need to understand why conversations fail |
| Call Analytics | Call volume, duration, disposition codes | Twilio Insights, call center platforms | For aggregate call patterns and volume planning |
| Evaluation Platforms | Pre-launch testing, regression testing, scenario coverage | Hamming | Before deployment and continuous testing |
Best practice: Use infrastructure APM + voice-native observability together. Datadog tells you servers are healthy; Hamming tells you conversations are healthy.
When to use Hamming specifically:
- You need to alert on voice-specific metrics (TTFW, WER, intent accuracy)
- You need turn-level visibility into conversations
- You need to replay production calls for debugging
- You need to correlate test failures with production incidents
- You're running voice agents at scale (100+ calls/day)
How Teams Implement This with Hamming
Hamming provides native support for the 4-Layer Monitoring Stack out of the box:
- Infrastructure Layer: Hamming tracks MOS, jitter, packet loss via audio stream analysis
- Execution Layer: Hamming measures TTFW, ASR WER, LLM latency P50/P90/P99, TTS latency
- Experience Layer: Hamming detects retry patterns, frustration markers, sentiment trajectory, abandonment
- Outcome Layer: Hamming tracks task completion, containment rate, compliance adherence
- Alerting: Hamming provides configurable alerts with severity tiers, cooldowns, and runbook links
- Dashboards: Hamming offers executive, operator, and investigation views with drill-down
- Call Replay: Hamming enables turn-by-turn replay of any production call with full annotations
- Regression Testing: Failed production calls automatically become test cases
How to Choose a Voice Agent Monitoring Platform
Use this checklist when evaluating voice agent monitoring platforms:
| Criterion | Weight | Questions to Ask |
|---|---|---|
| Metrics Coverage | 25% | Does it track all 4 layers? TTFW? WER? Sentiment? Task completion? |
| Real-Time Alerting | 20% | Can it detect issues in <60 seconds? Configurable severity? Cooldowns? |
| Investigation Tools | 20% | Can you replay calls? Turn-level annotations? Root cause analysis? |
| Integration | 15% | OpenTelemetry export? Works with your voice platform (Retell, Vapi, LiveKit)? |
| Testing + Monitoring | 10% | Does it connect pre-launch testing to production monitoring? Feedback loop? |
| Compliance | 10% | SOC 2? HIPAA? Data residency options? |
Minimum viable monitoring platform should have:
- TTFW tracking at P50/P90/P99
- ASR accuracy (WER) measurement
- Intent accuracy tracking
- Task completion rate
- Real-time alerting with <5 min detection
- Call replay for debugging
Related Guides
- Voice Agent Incident Response Runbook — 4-Stack framework for diagnosing and resolving outages
- How to Evaluate Voice Agents — Hamming's VOICE Framework for quality evaluation
- Voice Agent Observability — 4-Layer Observability Framework
- How to Optimize Latency in Voice Agents — Latency Optimization Cycle
- Voice Agent Testing Maturity Model — Level 4 = Continuous Monitoring
- How to Monitor Voice Agent Outages in Real-Time — Real-time alerting patterns
- Voice Agent Analytics to Improve CSAT — Four Pillars of Experience Analytics
- Anatomy of a Perfect Voice Agent Analytics Dashboard — Dashboard design principles
