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How to Evaluate AI Voice Agents Performance in Production with Hamming

Sumanyu Sharma
Sumanyu Sharma
August 27, 2025
How to Evaluate AI Voice Agents Performance in Production with Hamming

How to Evaluate AI Voice Agents Performance in Production with Hamming

Voice agents handle thousands of calls daily, it's inevitable that failures occur. However, most teams discover failures through customer complaints, after the call has ended. This reactive approach to AI voice agent evaluation means issues are fixed after they’ve already impacted user experience.

Here's how to evaluate AI voice agents in production with Hamming.

Set Up Real-Time Performance Tracking

Connect your voice infrastructure to Hamming through webhook integration in under 30 minutes:

const webhookConfig = {
  url: "https://api.hamming.ai/v1/voice/webhook",
  auth: {
    apiKey: process.env.HAMMING_API_KEY
  },
  events: [
    "call.started",
    "call.ended",
    "transcription.final",
    "error.occurred"
  ],
  metadata: {
    environment: "production",
    service: "voice-agent"
  }
}

These are the essential metrics to enable first:

  • Latency tracking: P50, P95, and P99 response times
  • Completion rates: Successful vs. failed interactions
  • Error categorization: Technical failures vs. conversation breakdowns

Set thresholds based on your use case:

| Use Case | Latency P95 | Completion Rate | Error Rate | |----------|-------------|-----------------|------------| | Customer Service | < 800ms | > 85% | < 5% | | Sales Calls | < 1000ms | > 75% | < 8% | | Technical Support | < 900ms | > 80% | < 6% |

Hamming tracks various voice-specific metrics like turn-level response time, time-to-first word, interruptions and silence gaps. These are indicators that correlate directly with user satisfaction. Grove AI maintains sub-800ms latency across 10,000+ daily production calls using these insights.

Monitor Metrics That Predict Success

Focus on these four metric layers that determine voice agent performance:

Layer 1: System Performance

Track latency across percentiles. P50 shows the median experience, P90 highlights slow tail performance, P95 and P99 uncover severe outliers, and the maximum exposes the absolute worst-case.

Configure alerts for:

  • Latency spikes above threshold
  • Availability drops below 99.9%
  • Error rates exceeding baseline

Layer 2: Conversation Mechanics

Natural conversation indicators:

  • Interruption rate: >1 per call signals timing problems
  • Silence duration: Gaps >2 seconds break flow
  • Turn-taking consistency: Measured by overlap patterns

Layer 3: Task Completion

Business outcomes:

  • Intent recognition accuracy
  • Task completion by flow type
  • Transfer rate to human agents
  • Handling time per task

A checkout flow needs 95% completion. Complex troubleshooting might accept 75%. Configure thresholds per workflow.

Layer 4: Sentiment Signals

Quality indicators:

  • Conversation scores
  • Frustration patterns (repeated phrases, tone changes)
  • Resolution signals

Configure monitoring across all layers:

| Metric Category | Critical Threshold | Alert Type | Hamming Feature | |-----------------|-------------------|------------|-----------------| | Latency P95 | > 800ms | Immediate | Auto-detect | | Interruptions | > 3 per call | Daily digest | Quality scoring | | Task Completion | < 85% | Immediate | Custom monitoring | | Quality Issues | > 2 per call | Review queue | AI evaluation |

Analyze Conversation Quality with AI Evaluations

Metrics show what happened. Quality analysis reveals why.

Start with pre-built evaluations:

  • Politeness and professionalism
  • Information accuracy
  • Knowledge base and prompt compliance
  • Escalation handling

Create custom evaluations for your needs:

evaluation_config = {
  "name": "appointment_accuracy",
  "criteria": [
    "Confirms correct date and time",
    "Verifies patient identity",
    "Mentions preparation instructions",
    "Offers reminder options"
  ],
  "fail_conditions": [
    "Books wrong department",
    "Misses identity verification",
    "Provides incorrect preparation"
  ],
  "severity": "high"
}

hamming.create_evaluation(evaluation_config)

Hamming's flow analysis identifies failure points. Common patterns include:

  • Agents excelling at simple requests but struggling with multi-step processes
  • Specific phrases that confuse the model
  • Edge cases missing from training data

Chain evaluations for complex scenarios:

  1. Check if medical information was discussed
  2. If yes → Verify HIPAA compliance
  3. If no → Assess proper redirection

This analysis runs on every call, revealing patterns manual testing misses.

Configure Intelligent Alert Systems

An effective alert system enables engineers to fix problems easily.

Set threshold-based alerts for immediate issues:

alert_rules:
  - metric: latency_p95
    threshold: 800ms
    window: 5_minutes
    severity: critical
    channel: pagerduty

  - metric: error_rate
    threshold: 5%
    window: 10_minutes
    severity: high
    channel: slack_engineering

  - metric: task_completion
    threshold: 80%
    window: 30_minutes
    severity: medium
    channel: slack_product

Configure anomaly detection for unusual patterns. Hamming identifies deviations from your baseline, for instance, a call duration increasing by 50% or transfer rates spiking during specific hours. Graduated quality alerts:

  • Single failure: Log for review
  • Pattern (3+ similar): Create ticket
  • Widespread issue: Immediate notification Match escalation to operational reality: | Issue Type | Detection Method | Alert Channel | Response Time | |------------|-----------------|---------------|---------------| | System down | Error rate > 50% | PagerDuty | < 1 minute | | Performance degradation | Latency > threshold | Slack | < 15 minutes | | Quality issues | Evaluation failures | Email digest | Daily | | Edge cases | Pattern detection | Jira | Next sprint |

Hamming correlates related alerts. When latency spikes coincide with increased transfers, you receive one incident notification with full context, not three separate alerts.

Connect to existing systems:

hamming.configureWebhook({
  url: "https://your-system.com/incidents",
  events: ["critical_alert", "quality_degradation"],
  include_transcript: true,
  include_analysis: true,
  custom_fields: {
    team: "voice-platform",
    service: "production-agent"
  }
});

Start simple. Refine based on actual response patterns.

Convert Production Issues into Test Cases

Every production failure becomes a permanent test case.

Capture exact failure scenarios:

test_case = {
  "name": "Handle appointment rescheduling during holiday",
  "scenario": "Customer calls to reschedule on Thanksgiving",
  "expected_behavior": "Inform about closure, offer next available",
  "actual_failure": "Agent attempted booking on closed day",
  "test_type": "Voice Character simulation",
  "frequency": "Every deployment"
}

hamming.create_test(test_case)

Voice Characters simulate real callers with various accents, speaking styles, and communication patterns. They test exact production scenarios plus variations you haven't encountered.

Prioritize test creation by impact:

| Issue Type | Test Priority | Automation Level | |------------|--------------|------------------| | Affects > 1% of calls | Critical | Every deployment | | Compliance violation | Critical | Every deployment | | Edge case (< 0.1%) | Medium | Weekly regression | | Minor quality issue | Low | Monthly review |

Use pattern-based testing. When agents fail with "Thanksgiving," test "Christmas," "New Year's," and "Easter" automatically. This approach prevents entire categories of failures.

Build Actionable Dashboards

Different roles in an organization need different views.

Engineering Dashboard:

  • Top: System health (latency histograms, error rates)
  • Middle: Technical metrics (API times, concurrent calls)
  • Bottom: Recent failures with recordings

Update frequency: Real-time

const engineeringDash = {
  name: "Production Engineering View",
  widgets: [
    { type: "latency_histogram", position: "top_left" },
    { type: "error_rate_timeline", position: "top_right" },
    { type: "concurrent_calls_gauge", position: "middle_left" },
    { type: "recent_failures_list", position: "bottom" }
  ],
  alerts: ["critical", "high"],
  refresh_rate: "1m"
};

Product Dashboard:

  • Top: Task completion by flow
  • Middle: Feature usage and success
  • Bottom: Journey analytics and drop-offs

Update frequency: Hourly

Executive Dashboard:

  • Top: Business KPIs (volume, resolution, cost)
  • Middle: Satisfaction and quality trends
  • Bottom: Month-over-month improvements

Update frequency: Daily

Always include:

  1. North Star Metric: Primary success indicator
  2. Leading indicators: Predict problems
  3. Lagging indicators: Confirm impact
  4. Comparative metrics: Week-over-week performance

A Live AI Voice Agent Performance Evaluation

The cost of blind operation compounds daily. Every unmonitored day means undetected issues, preventable problems, and missed improvements. Teams with proper monitoring learn faster and serve customers better.

Book a demo to see an AI voice agent performance evaluation live.