Voice Agent Testing for Call Centers: The Complete 2026 Guide

If you're running fewer than 500 calls per week through a single-purpose voice agent, this guide is probably overkill. Manual QA and basic monitoring will serve you fine. Save this article for later.

But if you're deploying voice AI into a contact center environment—handling thousands of calls daily, dealing with PCI-DSS or HIPAA requirements, or integrating with legacy IVR systems—the testing approach needs to change fundamentally. That's what this guide covers.

Quick filter: If a single compliance miss could get you fined, you need call‑center‑grade testing, not “LLM evals + spot checks.”

Call center voice agents aren't just "regular" voice agents with more calls. They're fundamentally different systems operating under different constraints.

A consumer-facing voice agent might handle dozens of conversations per day. A call center AI must handle thousands—while maintaining PCI-DSS compliance for payments, HIPAA compliance for healthcare, and integrating with legacy IVR systems that predate modern APIs. The testing requirements don't just scale linearly. They compound.

At Hamming, we've analyzed over 1 million call center interactions across 50+ deployments. The teams that succeed don't test their voice agents harder—they test them differently. This builds on Hamming's VOICE Framework for general voice agent evaluation, but adds call center-specific layers that address scale, compliance, and legacy integration. Here's what we've learned.

TL;DR: Test call center voice agents using Hamming's Call Center Testing Framework:

• Layer 1: Telephony Infrastructure — SIP reliability, call routing, failover (>99.9% success rate)
• Layer 2: Conversation Quality — FCR (75% target), AHT (<4 min), transfer rate (<15%)
• Layer 3: Compliance — PCI-DSS for payments, HIPAA for healthcare, state recording laws
• Layer 4: Scale & Load — Test at 2x expected peak capacity

Call centers can't afford downtime or compliance failures. This guide shows you how to test for both.

Related Guides:

• Voice Agent Testing Platforms Comparison — Hamming's Platform Selection Framework
• How to Evaluate Voice Agents — Hamming's VOICE Framework
• Conversational Flow Measurement — Hamming's Conversation Quality Framework
• Complete Voice Agent QA Platform — End-to-end testing capabilities
• Testing Voice Agents for Healthcare — HIPAA compliance deep-dive
• Testing Voice Agents for Financial Services — PCI-DSS compliance guide

Methodology Note: The benchmarks in this guide are derived from Hamming's analysis of 1M+ call center voice agent interactions across 50+ deployments (2025). Industry benchmarks are sourced from ICMI Contact Center Metrics Report and ContactBabel US Contact Center Decision-Makers' Guide. Compliance information was last verified January 2026.

Why Call Center Voice Agent Testing Is Different

Call center voice agents aren't just "regular" voice agents at higher volume. They face three unique challenges that fundamentally change testing requirements.

The Scale Challenge

Human call centers typically employ 100-10,000 agents. When you deploy AI, it must handle equivalent volume—but without the natural throttling that comes from limited human capacity.

Key differences:

• No downtime: Human agents take breaks, call in sick, go on vacation. AI runs 24/7.
• Peak load multipliers: Call centers experience 3-5x average load during peak hours (Monday mornings, after marketing campaigns, during crises).
• Instantaneous scaling: A human call center can route calls to queue when overwhelmed. AI must handle spikes instantly or fail publicly.

The Compliance Challenge

Consumer voice agents can afford to learn from mistakes. Call center AI cannot. Regulatory compliance isn't optional.

Critical requirements:

• PCI-DSS: Payment Card Industry Data Security Standard for handling payment information
• HIPAA: Health Insurance Portability and Accountability Act for protected health information
• State recording laws: 12 states require all-party consent before recording calls
• Industry regulations: Financial services (FINRA), insurance (state DOIs), healthcare (HIPAA)

A single compliance violation can result in fines, lawsuits, and regulatory shutdowns.

The Consistency Challenge

Here's a pain point we hear constantly from call center teams: test results are inconsistent, and pass/fail reasoning doesn't correlate with actual behavior.

We've seen QA teams abandon automated testing entirely because test cases pass or fail unpredictably, and the reasoning doesn't match what actually happened in the call. They end up re-testing manually anyway—which defeats the purpose.

This happens because most testing tools use cheap models that produce unreliable evaluations. Hamming uses more expensive models specifically because we need 95-96% agreement with human evaluators. Anything less makes automated testing pointless—you're just generating noise.

We’ve had teams show us “passing” test suites that still produced angry customer escalations. The mismatch usually comes down to evaluation quality, not model quality.

The Integration Challenge

Call centers run on legacy infrastructure that predates modern APIs. Your voice agent must integrate with:

• Legacy IVR systems: Built in the 2000s, often proprietary protocols
• CRM platforms: Salesforce, Zendesk, HubSpot—each with different data models
• Ticketing systems: For escalation and follow-up tracking
• Workforce management: Scheduling, forecasting, analytics
• Quality monitoring: Call recording, speech analytics, compliance tracking

These integrations aren't "nice to have"—they're table stakes for call center deployment.

Flaws but Not Dealbreakers

Before we dive into the framework, let me be honest about the limitations of comprehensive call center testing:

Load testing requires real compute costs. Running 10,000+ concurrent synthetic calls isn't free. Expect to budget $500-2,000 per major load test depending on duration and scale. The ROI is there—one production outage costs far more—but it's not zero upfront investment.

Initial setup takes longer than you expect. When we work with new call center deployments, baseline configuration typically takes 2-3 weeks, not 2-3 days. Compliance testing alone requires legal review of consent scripts, vendor BAA verification, and state-by-state routing validation.

You can't test everything before launch. Some edge cases only appear at scale. The goal isn't zero production issues—it's catching the catastrophic ones before they hit customers, and having monitoring in place to catch the rest quickly.

Compliance requirements evolve. PCI-DSS 4.0 introduced new requirements recently. HIPAA interpretations shift. State laws change. Testing frameworks need ongoing maintenance, not just initial setup.

These are real constraints. They don't make call center voice agent testing impossible—they make it an investment decision rather than a checkbox exercise.

The Call Center Voice Agent Testing Framework

Hamming's Call Center Testing Framework addresses the unique requirements of contact center deployments across four layers. Each layer builds on the previous one—you can't validate conversation quality if telephony infrastructure is failing.

Layer 1: Telephony Infrastructure Testing

Test the foundation: Can your voice agent reliably connect calls and maintain audio quality?

What to Test

SIP trunk reliability and failover:

• Primary trunk handles calls correctly
• Failover to secondary trunk occurs within 10 seconds
• Geographic routing works (route to nearest data center)
• Codec negotiation succeeds (G.711, G.729, Opus)

Call routing accuracy:

• Inbound calls route to correct agent/queue
• DID (Direct Inward Dialing) numbers map correctly
• Time-based routing works (business hours vs after-hours)
• Skill-based routing functions (Spanish language, technical support)

IVR handoff behavior:

• Warm transfer includes call context
• Cold transfer completes successfully
• Call recording continues across transfer
• Caller ID preserved through transfer

Audio codec compatibility:

• G.711 (most common, high quality)
• G.729 (compressed, lower bandwidth)
• Opus (modern, adaptive bitrate)

Enterprise tip: If you own your telephony stack (not just using Twilio), SIP-to-SIP integration enables advanced testing capabilities. You can inject custom headers to track which test call triggered which backend actions—headers that survive internally but get stripped over PSTN. This is particularly valuable for validating tool call correctness, not just conversational quality.

Key Metrics

Test Scenarios

Scenario 1: Normal Call Routing

• Make 100 test calls
• Verify all connect within 2 seconds
• Check audio quality (MOS >4.0)
• Confirm correct routing

Scenario 2: Primary SIP Trunk Failure

• Simulate trunk outage
• Verify failover to secondary within 10 seconds
• Confirm calls continue without dropping
• Validate audio quality maintained

Scenario 3: Geographic Failover

• Test calls from multiple regions (US East, West, EU, APAC)
• Verify routing to nearest data center
• Measure latency from each region
• Confirm no cross-region call routing unless failure

Scenario 4: Peak Load (2x Capacity)

• Generate 2x expected concurrent calls
• Measure call setup time degradation
• Track SIP success rate under load
• Monitor audio quality degradation

Scenario 5: Codec Negotiation

• Test calls forcing different codecs
• Verify fallback from Opus to G.711 to G.729
• Measure audio quality for each codec
• Confirm compatibility with carrier requirements

Important: Test with real phone calls, not just web calls. We've seen teams burn a launch because web call testing showed 300-400ms lower latency than real PSTN calls. Everything passed in testing, then failed in production because they hadn't accounted for real telephony latency. Always validate over PSTN, not just WebRTC.

Layer 2: Conversation Quality Testing

Once telephony works, test whether your AI actually solves customer problems. This layer extends Hamming's Conversational Flow Framework with call center-specific metrics like First Call Resolution and Average Handle Time.

The manual testing trap: Before automated testing, teams typically spend 2-5 minutes per test cycle just to identify if one thing near the end broke. Testing 9 pathways manually by calling cell phones means 18-45 minutes minimum for a single pass—and that has to repeat after every prompt change. We've seen teams where calls get stuck in limbo and don't terminate properly, adding debugging time on top of testing time.

Call Center-Specific Metrics

Understanding FCR (First Call Resolution)

FCR is the most important call center metric. It directly correlates with customer satisfaction and operational cost.

Formula:

FCR = (Issues Resolved on First Call / Total Issues) × 100

Worked Example:

• 1,000 calls handled
• 750 resolved on first call (no callback within 7 days)
• 250 required callback or escalation
• FCR = (750 / 1000) × 100 = 75%

How to measure in testing:

1. Define "resolution" criteria for each call type (e.g., password reset = user can log in)
2. Track callbacks within 7 days (industry standard)
3. Segment by issue complexity (simple vs complex)
4. Compare AI vs human performance on same call types

Why FCR matters:

• Each callback costs $5-15 in agent time
• Low FCR (50-60%) often indicates poor training or tooling
• High FCR (80%+) correlates with 90%+ CSAT

"The thing that surprised me most about call center testing," says Ishaan Rajan, who leads engineering at Hamming, "is that FCR is more predictive than any individual metric. Teams obsess over latency, but a fast agent that doesn't solve problems generates more callbacks—and callbacks are where you lose customers."

Understanding AHT (Average Handle Time)

AHT measures total time per call, including post-call work.

Formula:

AHT = (Total Talk Time + Hold Time + After-Call Work) / Total Calls

Worked Example:

• 1,000 calls
• 5,000 minutes talk time
• 1,000 minutes hold time
• 500 minutes after-call work (documentation, ticketing)
• Total: 6,500 minutes
• AHT = 6,500 / 1,000 = 6.5 minutes

AI advantage:

• Humans: 6 min average (3-4 min talk + 1-2 min after-call work)
• AI: 4 min target (3 min talk + 1 min automated documentation)
• Instant access to knowledge base (no hold time for lookups)

Trade-off to watch:

• Don't sacrifice FCR for low AHT
• A 2-minute call that requires a callback has effective AHT of 8+ minutes
• Target: Fast and complete resolution

Quality Scoring Approach

Step 1: Define resolution criteria per call type

Step 2: Track callbacks within 7 days

Industry standard: If customer calls back about same issue within 7 days, first call was not resolved.

Step 3: Segment by issue complexity

Don't average simple and complex together:

• Simple issues (password resets): 90%+ FCR expected
• Medium issues (billing): 75-80% FCR expected
• Complex issues (technical troubleshooting): 60-70% FCR expected

Step 4: Compare AI vs human on same call types

Fair comparison requires matching call types:

• Route same % of simple/medium/complex to both
• Match time of day (morning vs afternoon performance differs)
• Use same scripts/knowledge base
• Measure over 2+ weeks to smooth daily variance

Layer 3: Compliance Testing

I used to think compliance testing was a checkbox exercise—run through the requirements once, document everything, move on. After watching three deployments get delayed by compliance issues that surfaced late in testing, I've changed my approach entirely.

Compliance isn't optional. A single violation can shut down your call center.

PCI-DSS Requirements (Payment Card Industry)

If your voice agent handles credit cards, you must comply with PCI-DSS. For a deeper dive into financial services compliance, see our complete guide to testing voice agents for financial services.

Key Requirements:

Test Scenarios:

Scenario 1: Payment Collection Flow

1. Customer provides card number verbally
2. Agent captures and processes payment
3. Verify:
   • Transcript shows "XXXX-XXXX-XXXX-1234" (not full number)
   • Logs contain no PAN
   • Payment processor receives full number (transmission works)
   • Database stores only tokenized reference

Scenario 2: CVV Handling

1. Customer provides CVV (3-4 digit code)
2. Agent processes payment
3. Verify:
   • CVV never appears in transcript
   • CVV never written to logs
   • CVV transmitted directly to processor
   • No CVV in database (PCI-DSS 3.2.3 explicitly forbids storage)

Scenario 3: Transcript Redaction

1. Generate 100 test calls with card numbers
2. Export transcripts
3. Verify:
   • All card numbers redacted (not just most)
   • Redaction format consistent ("XXXX-XXXX-XXXX-1234")
   • No false negatives (16-digit numbers that aren't cards)
   • No false positives (phone numbers, account numbers)

Scenario 4: Access Control Audit

1. Attempt to access payment data with various user roles
2. Verify:
   • Only authorized roles can view masked data
   • No role can view full PAN (not even admins)
   • All access logged with timestamp, user, IP
   • Audit logs immutable (can't be deleted)

HIPAA Requirements (Healthcare)

If your voice agent handles Protected Health Information (PHI), you must comply with HIPAA. Healthcare has additional considerations beyond what we cover here—see our dedicated healthcare voice agent testing guide for appointment scheduling, prescription management, and clinical workflows.

Key Requirements:

What is PHI?

• Patient names
• Medical Record Numbers (MRNs)
• Diagnoses, conditions, symptoms
• Prescription information
• Appointment dates/times
• Insurance information
• Lab results, test results
• Any health-related information linked to an individual

Test Scenarios:

Scenario 1: Patient Information Collection

1. Patient provides name, DOB, MRN, symptoms
2. Agent schedules appointment
3. Verify:
   • All PHI encrypted in database (AES-256)
   • Transmission encrypted (TLS 1.2+)
   • Audit log records collection event
   • Only minimum necessary data collected

Scenario 2: Prescription Inquiry

1. Patient asks about medication refill
2. Agent accesses prescription history
3. Verify:
   • Access logged (who, what, when)
   • Only current prescriptions shown (not full history)
   • No prescription details in transcript (redacted)
   • Patient identity verified before access

Scenario 3: PHI Redaction in Transcripts

1. Generate 100 test calls with PHI
2. Export transcripts
3. Verify:
   • Patient names redacted ("Patient A")
   • MRNs redacted ("MRN-XXXX")
   • Diagnoses redacted or generalized
   • Prescription names redacted
   • Dates preserved (appointment scheduling needs them)

Scenario 4: Business Associate Agreement (BAA) Compliance

1. List all vendors with PHI access (STT, TTS, LLM, storage)
2. Verify:
   • BAA signed with each vendor
   • BAA covers all HIPAA requirements
   • Vendor is willing to sign BAA (some won't)
   • Subprocessor list documented

Common HIPAA Pitfall:

Many LLM providers (OpenAI, Anthropic, etc.) will NOT sign BAAs for voice use cases because they can't guarantee zero PHI leakage in model outputs. You may need:

• HIPAA-compliant LLM (Azure OpenAI with BAA)
• On-premise model deployment
• PHI filtering before LLM call

State Recording Laws

Call recording consent requirements vary by state.

One-Party Consent States (38 states): Only one party to the conversation needs to consent to recording. Your voice agent is a party, so you can record without explicit customer consent.

Two-Party (All-Party) Consent States (12 states): All parties must consent before recording. You must play a consent script before recording starts.

Legal Disclaimer: Recording consent laws change frequently. The penalties listed above were verified as of January 2026 but may have changed. Always consult qualified legal counsel before deploying call recording in any jurisdiction. This guide is for informational purposes only and does not constitute legal advice.

Test Scenarios:

Scenario 1: Geographic Routing Detection

1. Make test calls from each two-party consent state
2. Verify:
   • System detects caller location (ANI, area code, IP)
   • Consent script plays before recording starts
   • If consent declined, recording stops (call can continue)
   • Consent logged in audit trail

Scenario 2: Consent Script Validation

1. Review consent script for legal compliance
2. Verify:
   • Clear statement that call may be recorded
   • Opportunity to decline (opt-out)
   • No deceptive language
   • Plays in caller's language (if multilingual)

Example Consent Script:

"This call may be recorded for quality and training purposes. If you do not wish to be recorded, please say 'do not record' now or press 1."

Scenario 3: Multi-State Testing

1. Generate calls from all 50 states
2. Verify:
   • Two-party states trigger consent script
   • One-party states skip consent (faster call start)
   • Logging correctly identifies state of call origin

Layer 4: Scale & Load Testing

Call centers can't afford downtime during peak hours. Test at 2x expected capacity. Our production reliability testing guide covers the 3-Pillar Framework (Load, Regression, A/B) in detail—this section focuses on call center-specific load patterns.

Why 2x Capacity Matters

Remember the compliance testing we covered in Layer 3? Load testing is where compliance requirements get stress-tested. A system that redacts card numbers correctly at 100 calls/minute might fail to redact under 1,000 calls/minute load.

Last holiday season, we saw a retail deployment handle pre-holiday load perfectly during testing—then fail on December 26th when return calls spiked to 3x normal volume. They'd tested at 150% capacity but not 300%. Now we recommend 2x as a minimum, with spike tests at 3x for retail and e-commerce.

Predictable peaks:

• Monday mornings (weekly spike)
• First of month (billing inquiries)
• Post-marketing campaign (sales surge)
• Service outages (support flood)

Unpredictable spikes:

• PR crisis (press coverage drives calls)
• Viral social media (complaint goes viral)
• System failure (users call about outage)
• Competitor outage (customers switching)

No graceful degradation:

• E-commerce site can show "add to cart" loading spinner
• Call center can't put customers on infinite hold
• Abandoned calls = lost revenue + angry customers

Load Testing Methodology

5-Phase Approach:

Phase 1: Baseline

• Measure normal performance (no load)
• Establish latency P50, P95, P99
• Record error rate (<0.1% expected)
• Capture resource utilization (CPU, memory, network)

Phase 2: Gradual Ramp

• Increase load from 0% to 100% over 30 minutes
• Measure degradation at 50%, 75%, 100%
• Continue to 150%, 200%
• Identify point where performance degrades >50%

Phase 3: Spike Test

• Sudden jump from 50% to 200% load
• Hold for 5 minutes
• Measure:
  • Call setup time during spike
  • Error rate during spike
  • Recovery time after spike drops

Phase 4: Soak Test

• Sustained load at 100% for 4+ hours
• Identifies memory leaks, resource exhaustion
• Common issues:
  • Database connection pool exhaustion
  • Memory leaks in long-running processes
  • File descriptor limits
  • Cache invalidation failures

Phase 5: Recovery Test

• Drop load from 200% to 0%
• Verify system returns to baseline
• Check for:
  • Stuck connections
  • Zombie processes
  • Database deadlocks
  • Cache inconsistencies

Metrics to Track Under Load

Load Testing Tools

For SIP/telephony load:

• SIPp: Industry standard, scriptable, handles 10K+ calls
• Voip Load: Commercial, GUI-based, realistic call patterns

For HTTP/API load:

• k6: Modern, scriptable in JavaScript, great graphs
• Locust: Python-based, distributed load generation
• Artillery: Simple, YAML config, CI/CD friendly

Call Center KPI Benchmarks

Use these benchmarks to set targets and measure AI vs human performance.

Comprehensive Benchmark Table

Note on Occupancy Rate: AI agents don't have occupancy constraints. A human agent at 85% occupancy is near burnout. An AI agent can handle unlimited concurrent calls within infrastructure limits.

Service Level Explained: "80/20 service level" means 80% of calls answered within 20 seconds. This is an industry standard benchmark.

Benchmarks by Industry

Source: ICMI (International Customer Management Institute), ContactBabel industry reports

Implementation Checklist

Team sizing context: Mid-size call centers typically have 6 QA engineers total, with 2 dedicated to voice agent testing. Enterprise contact centers may have 10-12 offshore QA engineers supporting the voice AI team. You don't need a huge team—but you do need dedicated resources who understand both the compliance requirements and the conversation quality metrics.

Pre-Launch Testing (4-6 weeks before)

Week 1-2: Baseline & Requirements

• Document current human agent metrics

  • Export last 90 days of call data
  • Calculate FCR, AHT, transfer rate, CSAT by call type
  • Identify best-performing agents (use as benchmark)
  • Segment by simple/medium/complex issues

• Map all call types and volumes

  • List all call types (password reset, billing, technical support, etc.)
  • Calculate volume for each (calls/day, % of total)
  • Identify peak hours and days
  • Document seasonal patterns (holidays, end-of-month)

• Identify compliance requirements

  • List all regulations (PCI-DSS, HIPAA, state laws)
  • Document PHI/PCI handling requirements
  • Review recording consent requirements by state
  • Confirm vendor BAAs in place

• Set up test environment

  • Provision test telephony infrastructure (separate from prod)
  • Configure test SIP trunks
  • Set up test phone numbers
  • Deploy voice agent to test environment
  • Configure monitoring and logging

Week 3-4: Functional Testing

• Test each call scenario

  • Create test script for each call type
  • Execute 20+ tests per call type
  • Measure FCR, AHT, accuracy
  • Document failure modes
  • Test multi-turn sequences (book → cancel → reschedule) that maintain context across calls
  • Verify calls terminate properly—we've seen agents get stuck in limbo without hanging up

• Verify CRM integration

  • Test customer lookup (by phone, email, account ID)
  • Verify ticket creation (all fields populated)
  • Test note logging (transcript sync)
  • Validate callback scheduling

• Test IVR handoffs

  • Warm transfer (with context)
  • Cold transfer (no context)
  • Blind transfer (no agent greeting)
  • Transfer to specific queue/skill
  • Recording continuity across transfer

• Validate compliance scripts

  • Recording consent (two-party states)
  • Payment disclosure (PCI-DSS)
  • HIPAA notice (healthcare)
  • TCPA compliance (automated calling)

Week 5: Load Testing

• Run 100% load test

  • Generate concurrent calls matching peak volume
  • Run for 1 hour
  • Measure latency P50, P95, P99
  • Track error rate

• Run 150% load test

  • Generate 1.5x peak volume
  • Run for 30 minutes
  • Measure performance degradation
  • Identify bottlenecks

• Run 200% spike test

  • Sudden jump from 50% to 200%
  • Hold for 5 minutes
  • Measure spike handling
  • Verify recovery

• Run 4-hour soak test

  • Sustained 100% load for 4+ hours
  • Monitor for memory leaks
  • Check connection pool exhaustion
  • Validate database performance

Week 6: Compliance Validation

• PCI-DSS audit (if applicable)

  • Verify no card numbers in transcripts
  • Confirm TLS 1.2+ encryption
  • Test access controls (RBAC)
  • Validate redaction accuracy (100% of test cases)
  • Verify no CVV storage

• HIPAA audit (if applicable)

  • Verify PHI encryption (AES-256)
  • Test minimum necessary principle
  • Audit access logging (completeness)
  • Confirm BAAs with all vendors
  • Test PHI redaction (100% of test cases)

• Recording consent verification

  • Test calls from all two-party consent states
  • Verify consent script plays
  • Test opt-out functionality
  • Confirm logging of consent/decline

• Transcript redaction check

  • Generate 100+ test calls with sensitive data
  • Export and review all transcripts
  • Verify 100% redaction (no false negatives)
  • Check for false positives (over-redaction)

Post-Launch Monitoring

• Real-time dashboard for key metrics

  • FCR (rolling 24 hours)
  • AHT (current hour)
  • Transfer rate (current hour)
  • Error rate (last 15 minutes)
  • Call volume (current vs expected)
  • SIP success rate (last hour)

• Alert thresholds configured

  • FCR drops below 70% (warning)
  • FCR drops below 65% (critical)
  • Error rate >1% (warning)
  • Error rate >2% (critical)
  • SIP success rate <99% (warning)
  • SIP success rate <98% (critical)

• Daily FCR and AHT reports

  • Email to stakeholders at 9 AM
  • Segment by call type
  • Compare to human baseline
  • Highlight outliers

• Weekly compliance audits

  • Review 50 random transcripts
  • Check redaction accuracy
  • Verify consent logging
  • Audit access logs

• Monthly performance reviews

  • Compare to human agents
  • Analyze trends (improving or degrading?)
  • Review customer feedback
  • Identify improvement opportunities

Common Call Center Testing Mistakes

Most teams assume the biggest testing risk is technical—latency, accuracy, or scale issues. Actually, the failures that shut down deployments are usually compliance and integration problems. Technical issues cause bad calls; compliance issues cause lawsuits. A complete voice agent QA platform should catch both before they reach production.

Learn from others' failures. These mistakes have shut down call center deployments.

A/B Testing AI vs Human Agents

The only way to prove AI works: direct comparison on identical call types.

Setup Methodology

Step 1: Route 10% of calls to AI

• Use random routing (not cherry-picking)
• Ensure AI and human get same call type distribution
• Match time of day (morning vs afternoon performance differs)

Step 2: Match call types and times

Step 3: Track identical metrics

• Use same FCR definition (callback within 7 days)
• Use same CSAT survey (sent same time after call)
• Use same AHT calculation (talk + hold + after-call work)

Step 4: Run for 2+ weeks

• Week 1 may show novelty effects (bias)
• 2+ weeks smooths daily variance
• Stop early if error rate >5% (safety threshold)

Comparison Framework

Decision Framework:

Expand AI (increase from 10% to 50%):

• FCR within 5% of human baseline (67-77% in example above)
• AHT equal or faster (6.2 min or less)
• CSAT within 3% of human (75-81%)
• Error rate <1%
• No compliance violations in 2-week test

Needs improvement (stay at 10%, iterate):

• FCR >5% below human (<67%)
• AHT slower than human (>6.2 min)
• CSAT >3% below human (<75%)
• Error rate 1-3%
• Minor compliance issues (fixable)

Roll back (return to 0%, redesign):

• FCR >10% below human (<62%)
• CSAT >5% below human (<73%)
• Error rate >3%
• Compliance violations

Cost Comparison

Human Agent Cost Structure:

• Base salary: $35,000/year
• Benefits (30%): $10,500/year
• Training: $2,000/year
• Manager overhead (1:10 ratio): $5,000/year
• Technology (seat license, headset): $1,500/year
• Total: $54,000/year

Calls handled:

• 7.5 hours/day x 80% occupancy = 6 hours productive
• 6 hours x 60 min/hour / 6 min AHT = 60 calls/day
• 60 calls/day x 250 working days = 15,000 calls/year
• Cost per call: $54,000 / 15,000 = $3.60

AI Agent Cost Structure:

• Platform fee: $0.15/min
• STT (Speech-to-Text): $0.02/min
• TTS (Text-to-Speech): $0.03/min
• LLM: $0.05/min
• Infrastructure: $0.01/min
• Total: $0.26/min

Cost per call:

• 4 min AHT x $0.26/min = $1.04/call

Savings:

• Human: $3.60/call
• AI: $1.04/call
• Savings: $2.56/call (71%)

ROI Calculation:

• 10,000 calls/day call center
• 5,000 calls/day to AI (50% routing after expansion)
• $2.56 savings/call x 5,000 calls/day x 365 days = $4.67M/year savings

The Tension We Haven't Fully Resolved

There's an honest tension in call center voice agent testing that we still grapple with: thoroughness versus speed to market.

Comprehensive testing (all four layers, full compliance validation, 200% load tests) takes 4-6 weeks. But business pressure often demands faster deployment. We've seen teams skip load testing to hit a launch date—and then scramble when Black Friday traffic crashes their system.

We don't have a perfect answer. Different teams land in different places on this tradeoff depending on their risk tolerance, regulatory environment, and competitive pressure.

Our current recommendation: Never skip Layer 3 (compliance). Regulatory violations have existential consequences. Layers 1, 2, and 4 can be compressed if you accept the risk—but go in with eyes open about what you're skipping and why.

This is something we're still refining as we see more deployments. The right balance probably varies by industry: healthcare can't cut corners on compliance, while an internal IT helpdesk has more flexibility.

Frequently Asked Questions

What makes call center voice agent testing different from regular voice agent testing?

Call centers require testing at scale (10K+ calls/day), compliance validation (PCI-DSS, HIPAA), and integration with legacy systems. According to Hamming's analysis of 50+ deployments, teams that test only happy-path scenarios miss 60%+ of production issues. Call center testing must address four layers: telephony infrastructure, conversation quality, compliance, and scale.

What is a good First Call Resolution (FCR) rate for AI voice agents?

According to Hamming's benchmarks, AI voice agents should target 75% FCR—matching or exceeding the 70% human average. Excellent performance is 80%+ FCR. FCR is calculated as (Issues Resolved on First Call / Total Issues) × 100. Track callbacks within 7 days to measure true resolution.

How do I test PCI-DSS compliance for payment-handling voice agents?

Test four requirements: (1) Verify no full card numbers appear in transcripts, (2) Confirm TLS encryption for transmission, (3) Audit access controls, and (4) Validate card number masking in UI. According to Hamming's compliance framework, never store CVV numbers—they should be transmitted directly to payment processor and never logged.

What load testing methodology should I use for call center AI?

Hamming recommends a 5-phase approach: (1) Baseline at normal load, (2) Gradual ramp to 100%, 150%, 200%, (3) Spike test (sudden jump to 200%), (4) Soak test (sustained 4+ hours), and (5) Recovery validation. Always test at 2x expected peak capacity—call centers experience unpredictable volume spikes from marketing campaigns.

How do I compare AI agent performance to human agents?

Route 10% of calls to AI, match call types and times, track identical metrics for 2+ weeks. According to Hamming's A/B testing framework, AI is ready for expansion when: FCR is within 5% of human baseline, AHT is 20%+ faster, and CSAT is within 3% of human performance.

What Average Handle Time (AHT) should I target for AI voice agents?

AI agents should target <4 minutes AHT—33% faster than the 6-minute human average. Excellent performance is <3 minutes. Calculate AHT as (Total Talk Time + Hold Time + After-Call Work) / Total Calls. However, don't sacrifice FCR for speed—a fast call that requires a callback increases total handle time.

What are the two-party consent states for call recording?

12 states require all-party consent: California, Connecticut, Florida, Illinois, Maryland, Massachusetts, Michigan, Montana, New Hampshire, Oregon, Pennsylvania, and Washington. According to Hamming's compliance testing framework, verify your consent script plays before recording in these states.

How do I test HIPAA compliance for healthcare voice agents?

Test four requirements: (1) Verify PHI encryption at rest and in transit, (2) Audit "minimum necessary" data collection, (3) Confirm complete access logging, and (4) Verify Business Associate Agreement (BAA) compliance with all vendors. According to Hamming's healthcare deployments, test PHI redaction in transcripts—patient names, MRNs, and diagnoses should be masked.