An engineering team at a fintech company had a voice agent that passed every unit test. Synthetic evaluations showed 96% intent accuracy. Latency stayed under 400ms. Their staging dashboards were green.
Then they deployed to production.
Within 48 hours, escalation rates tripled. Customers were repeating themselves. The agent kept asking for account numbers it had already received. Call recordings revealed the problem: background office noise was corrupting ASR transcriptions, which fed garbled text to the LLM, which generated confused responses.
No single component had failed. The pipeline had failed.
This is the fundamental challenge of debugging voice agents. Errors do not stay isolated—they cascade across sequential components, compounding at each stage. According to Hamming's analysis of 4M+ production voice agent calls, teams that implement turn-level debugging with audio-attached traces resolve production incidents 3x faster than those relying on transcript-only logs and aggregate dashboards.
TL;DR: Voice agent debugging requires audio-level analysis, turn-by-turn tracing, and production replay workflows. Target less than 500ms end-to-end latency (STT 100-200ms, LLM 150-300ms, TTS 50-100ms). Alert on P90 latency exceeding 3.5s, success rate below 80%, or WER above 5%. Convert every production failure into a regression test. Use Hamming's 4-Layer Observability Framework (Infrastructure → Audio Quality → Turn-Level → Conversation-Level) for full-stack debugging coverage.
Methodology Note: The debugging workflows, thresholds, and patterns in this guide are derived from Hamming's analysis of 4M+ production voice agent calls across 10K+ voice agents (2025-2026).Patterns represent common failure modes across healthcare, financial services, e-commerce, and customer support deployments. Specific thresholds may vary by use case complexity and acoustic environment.
Last Updated: February 2026
Related Guides:
- Voice Agent Observability: End-to-End Tracing — Distributed tracing across STT, LLM, and TTS layers
- Voice Agent Troubleshooting: Complete Diagnostic Checklist — Systematic diagnosis for production failures
- Voice Agent Monitoring KPIs — 10 production metrics with formulas and benchmarks
- Voice Agent Incident Response Runbook — Step-by-step playbook for production incidents
Voice Agent Debugging: Master Reference Table
Before diving into each debugging domain, this reference table summarizes the key metrics, thresholds, and actions that define effective voice agent debugging.
| Debugging Domain | Key Metric | Formula / Method | Healthy | Warning | Critical | Action When Critical |
|---|---|---|---|---|---|---|
| Pipeline Accuracy | Voice Intent Error Rate | 1 - (ASR Accuracy x NLU Accuracy) | less than 5% | 5-10% | greater than 10% | Isolate failing component via turn-level trace |
| End-to-End Latency | P95 Response Time | STT + LLM + TTS | less than 500ms | 500-800ms | greater than 1200ms | Profile each stage independently |
| ASR Quality | Word Error Rate (WER) | (S + D + I) / Total Words x 100 | less than 5% | 5-8% | greater than 8% | Check audio quality, retrain ASR model |
| Intent Recognition | First-Turn Intent Accuracy | Correct first-turn intents / Total first turns x 100 | greater than 97% | 93-97% | less than 93% | Expand training data, fix ASR upstream |
| Confidence Monitoring | Low-Confidence Rate | Turns with confidence less than 0.6 / Total turns x 100 | less than 5% | 5-15% | greater than 15% | Add fallback logic, review training coverage |
| Fallback Rate | Fallback Trigger Frequency | Fallback responses / Total responses x 100 | less than 5% | 5-15% | greater than 15% | Analyze by intent category, expand coverage |
| Task Completion | First Call Resolution | Resolved calls / Total calls x 100 | greater than 85% | 75-85% | less than 75% | Drill down by failure category |
| TTS Quality | Mean Opinion Score | Human or automated rating (1-5 scale) | 4.3-4.5 | 3.8-4.3 | less than 3.8 | Switch TTS provider or tune voice params |
How the STT, LLM, and TTS Pipeline Works
A voice agent processes every user utterance through three sequential stages: Speech-to-Text (STT) transcribes the audio waveform into text, the Large Language Model (LLM) interprets meaning and generates a response, and Text-to-Speech (TTS) converts that response back into audio. Each stage depends entirely on the output of the previous one.
The pipeline flow:
User Speech → [STT: 100-200ms] → Transcript → [LLM: 150-300ms] → Response Text → [TTS: 50-100ms] → Agent SpeechThis sequential dependency is what makes voice agents uniquely difficult to debug compared to text-based systems. A text chatbot only has one inference step. A voice agent has three, each with its own failure modes, latency characteristics, and quality metrics.
Where Errors Originate and Cascade
The compounding nature of pipeline errors is captured by a simple formula:
Voice Intent Error Rate = 1 - (ASR Accuracy × NLU Accuracy)At 95% ASR accuracy and 95% NLU accuracy, your end-to-end intent accuracy is not 95%—it is 90.25%. At 90% ASR and 90% NLU, you drop to 81%. Errors multiply, they do not add.
This means a "small" degradation in ASR quality from 95% to 90% does not cost you 5 percentage points of intent accuracy—it costs you nearly 10 when combined with downstream NLU errors. Teams that debug each component in isolation often miss this compounding effect entirely.
Latency Budget Across Components
To maintain natural conversational flow, the total pipeline must complete in under 500ms. Beyond that threshold, users perceive the agent as robotic or unresponsive. Here is how the latency budget typically breaks down:
| Component | Target Latency | Typical Range | What Causes Spikes |
|---|---|---|---|
| STT | 100-200ms | 80-350ms | Long utterances, background noise, cold starts |
| LLM | 150-300ms | 100-600ms | Complex reasoning, long context, rate limiting |
| TTS | 50-100ms | 40-200ms | Long responses, voice cloning overhead |
| Network/overhead | 20-50ms | 10-100ms | Region latency, serialization |
| Total | less than 500ms | 230-1250ms | Any single spike cascades |
For a deeper treatment of latency diagnosis and optimization, see Voice AI Latency: What's Fast, What's Slow, and How to Fix It.
How to Analyze Real-Time Call Logs for Voice Agent Debugging
Real-time call log analysis is the foundation of voice agent debugging. Every production issue ultimately traces back to a specific turn in a specific call where something went wrong. The question is whether your logging captures enough detail to pinpoint what.
Turn-Level Logging Fundamentals
A turn is one back-and-forth exchange: the user says something, the agent responds. Each turn is the atomic unit of debugging and should capture:
- STT output — The raw transcription with word-level timestamps and confidence scores
- Intent classification — The detected intent, confidence, and any slot values extracted
- LLM input/output — The full prompt (including conversation history) and generated response
- Tool calls — Any function calls triggered, their inputs, outputs, and latency
- TTS generation — The synthesized audio, voice parameters, and generation time
- Timing data — Start/end timestamps for each pipeline stage
Without turn-level granularity, you are debugging with aggregate metrics that average away the exact failures you need to find.
Structuring Logs for Effective Debugging
Effective voice agent logs follow a structured schema that enables both real-time alerting and post-hoc analysis. Each log entry should include:
- Call ID and turn number for correlation across distributed components
- Timestamps at millisecond resolution for each pipeline stage boundary
- Confidence scores from STT and intent classification
- Full conversation context available to the LLM at each turn
- Audio attachment references linking to the actual recorded audio segment
- Error flags and exception traces when any component fails
For a comprehensive treatment of logging architecture, including storage, retention, and compliance considerations, see Logging and Analytics Architecture for Voice Agents.
Why Audio Attachments Matter More Than Transcripts
Transcript-only logs are fundamentally incomplete for voice agent debugging. Audio recordings reveal critical information that transcripts completely miss:
- Pauses and hesitations — A 3-second pause before "yes" often means uncertainty, not agreement
- Background noise — Office chatter, traffic, or music that degrades ASR accuracy
- Interruptions and barge-ins — When users talk over the agent, indicating frustration
- Tone and prosody — Sarcasm, confusion, or anger that changes the meaning of words
- Acoustic artifacts — Echo, clipping, or codec issues that corrupt the audio signal
Teams using Hamming's end-to-end tracing attach audio segments directly to each turn in the trace, enabling one-click replay of any production failure alongside the full pipeline telemetry.
How to Identify and Fix Missed Intents in Voice Agents
Missed intents are the most common failure mode in production voice agents, and they are significantly harder to diagnose in voice systems than in text-based chatbots. Voice agents face 3-10x higher intent error rates than equivalent text systems due to the ASR cascade effect described above.
First-Turn Intent Accuracy (FTIA)
First-Turn Intent Accuracy measures whether the agent correctly identifies the user's intent on their very first utterance. This is the single most predictive metric for conversation success:
FTIA = (Correct first-turn intents / Total conversations) × 100Target: greater than 97% FTIA. Research across production deployments shows that an incorrect first-turn intent leads to 4x higher abandonment rates. When the agent misunderstands the opening request, users lose confidence and either repeat themselves, escalate, or hang up.
| FTIA Range | Impact | User Behavior |
|---|---|---|
| greater than 97% | Minimal friction | Natural conversation flow |
| 93-97% | Noticeable errors | Users rephrase, slight frustration |
| 88-93% | Significant friction | Repeated attempts, rising abandonment |
| less than 88% | Systemic failure | High escalation, user distrust |
For a deeper dive into intent recognition testing at scale, see Intent Recognition for Voice Agents: Testing at Scale.
Root Causes of Intent Classification Failures
Intent classification failures in voice agents have three primary root causes:
1. ASR Transcription Errors The most common cause. When the STT engine produces "I want to cancel my subscription" as "I want to counsel my description," no amount of NLU sophistication will recover the correct intent. This is where the 7 ASR Failure Modes in Production become critical to understand.
2. Out-of-Scope Queries Users ask questions the agent was never trained to handle. These surface as low-confidence classifications or incorrect mappings to the "nearest" intent. Monitor out-of-scope rates by tracking queries where the top intent confidence falls below 0.4.
3. Insufficient Training Data Intent classifiers underperform on utterance patterns they have not seen. Voice-specific phrasing ("Uh, yeah, so I need to, like, change my address?") differs substantially from the clean text examples most training datasets contain.
How Confidence Scores Help Diagnose Voice Agent Issues
Confidence scores from STT and intent classification stages act as early warning signals, enabling fallback strategies before complete user-facing failures occur. Monitoring confidence distributions—not just averages—reveals problems that aggregate metrics hide.
Tracking Confidence Across Pipeline Stages
Every pipeline stage produces a confidence signal:
- STT confidence: How certain the speech recognizer is about the transcription (0.0-1.0)
- Intent confidence: How certain the classifier is about the detected intent (0.0-1.0)
- Slot confidence: How certain the entity extractor is about extracted values (0.0-1.0)
Flag any turn where STT or intent confidence falls below 0.6 for human review or fallback logic. Turns in this zone are unreliable enough that the agent should either ask for clarification or route to a human.
Using Confidence Thresholds for Fallback Triggers
Link declining confidence scores to actionable triggers:
| Confidence Range | Recommended Action | Signal |
|---|---|---|
| 0.9-1.0 | Proceed normally | High certainty |
| 0.7-0.9 | Proceed with implicit confirmation | Moderate certainty |
| 0.5-0.7 | Explicit confirmation ("Did you say...?") | Low certainty |
| 0.3-0.5 | Re-prompt ("Could you repeat that?") | Very low certainty |
| less than 0.3 | Escalate to human agent | Unreliable |
When confidence drops correlate with rising repetition rates and escalation frequency, you have a systemic issue—not isolated bad calls.
Confidence Score Patterns That Signal Systemic Problems
Watch for declining confidence across conversation turns within a single call. When the STT confidence starts at 0.85 on turn 1 and drops to 0.6 by turn 5, it signals cumulative confusion—possibly from the agent's own TTS output bleeding into the microphone, or from the user becoming increasingly frustrated and speaking less clearly.
This pattern often appears without any single turn triggering a failure threshold, making it invisible to turn-level alerting alone. You need conversation-level trend analysis to catch it.
How to Monitor Fallback Patterns in Voice Agents
Fallback responses—"I didn't catch that," "Could you repeat that?", or silent transfers to human agents—are the visible symptoms of intent coverage gaps. Monitoring fallback frequency by category reveals where your agent's knowledge or recognition capability is weakest.
Tracking Fallback Rates by Intent Category
Aggregate fallback rates hide the real story. A 10% overall fallback rate might break down as:
| Intent Category | Fallback Rate | Interpretation |
|---|---|---|
| Billing inquiries | 5% | Well-covered, minor gaps |
| Account changes | 8% | Adequate but needs expansion |
| Technical support | 25% | Severe coverage gap |
| Product questions | 18% | Moderate gap, needs training data |
| Scheduling | 3% | Well-covered |
When 25% of technical support queries hit fallback, you do not need to retrain your entire model—you need targeted training data for technical support intents specifically.
Designing Effective Fallback Responses
Effective fallback flows follow a three-step escalation pattern:
- Re-prompt with context: "I didn't quite catch that. Were you asking about your billing statement or a recent charge?"
- Narrow the scope: "I can help with billing, account changes, or scheduling. Which of these is closest to what you need?"
- Graceful escalation: "Let me connect you with a specialist who can help with that. One moment."
Each step should be logged with the original utterance, the fallback reason, and the resolution path for post-hoc analysis.
When Fallback Rates Indicate Systemic Issues
Fallback rates above 15% across multiple categories point to problems beyond individual intent gaps:
- ASR degradation: A new user demographic or acoustic environment is producing transcriptions outside the training distribution
- Intent schema mismatch: The intent taxonomy does not match how users actually phrase requests
- Model drift: The underlying LLM or intent classifier has shifted behavior after an update
These require architectural investigation, not just more training data. Start with the Voice Agent Drift Detection Guide to identify whether the root cause is gradual model degradation.
How to Set Up Error Dashboards for Voice Agents
Generic APM dashboards miss the majority of voice-specific failures because they monitor infrastructure health, not conversation quality. Voice agent error dashboards must surface metrics across four distinct layers to capture the full failure space.
Hamming's 4-Layer Observability Framework for Voice Agent Debugging
Effective voice agent debugging requires visibility across four layers, each capturing a different class of failure:
Layer 1: Infrastructure
- System uptime, API availability, network latency
- CPU/memory utilization, connection pool health
- Provider status (STT, LLM, TTS endpoint availability)
Layer 2: Audio Quality
- Signal-to-noise ratio, echo detection, clipping events
- Codec quality, packet loss, jitter
- Background noise classification and impact on ASR
Layer 3: Turn-Level Execution
- STT latency and confidence per turn
- Intent classification accuracy and confidence
- LLM response time and token usage
- TTS generation time and quality score
- Tool call success rates and latency
Layer 4: Conversation-Level Outcomes
- Task completion rate, first call resolution
- Escalation frequency and reasons
- User sentiment trajectory across turns
- Context retention accuracy
For a complete guide to implementing this framework, see Voice Agent Monitoring: The Complete Platform Guide.
Essential Dashboard Metrics
Every voice agent error dashboard should include these core metrics, updated in real time:
- Turn-taking latency (P50, P90, P95) — Time from end of user speech to start of agent speech
- Interruption rate — Percentage of turns where the user interrupts the agent
- Time to First Word (TTFW) — Latency before the agent starts speaking
- Task completion rate — Percentage of calls achieving the business goal
- Escalation frequency — Rate of transfers to human agents
- WER trend — Word Error Rate tracked over rolling windows
- Intent accuracy by category — Broken down by intent, not just overall
- Fallback trigger rate — How often the agent fails to classify an intent
- Confidence score distribution — Histogram, not average
- Compliance violation count — Policy breaches detected per time window
See Voice Agent Monitoring KPIs: 10 Production Metrics for formulas and benchmarks for each metric, and Real-Time Voice Analytics Dashboards for dashboard layout patterns.
Real-Time vs. Batch Analytics
Real-time dashboards (sub-second to 1-minute refresh) surface acute failures as they happen:
- Missed intents spiking on a specific intent category
- Latency degradation indicating a provider issue
- Routing failures from configuration errors
- Sudden WER increases from audio quality problems
Batch analytics (hourly to daily aggregation) reveal trends and drift:
- Gradual intent accuracy degradation over weeks
- Seasonal patterns in call volume and failure rates
- Training data coverage gaps across user demographics
- Long-term TTS quality trends and user satisfaction correlation
Both are necessary. Real-time catches fires. Batch catches rot.
Drill-Down Capabilities for Root Cause Analysis
The most valuable dashboard feature is one-click drill-down from any metric directly to the underlying evidence:
- Click on a latency spike → See the specific calls and turns that contributed
- Click on an intent accuracy drop → See the misclassified utterances with audio
- Click on an escalation → See the full conversation trace with turn-level annotations
Without drill-down, dashboards tell you something is wrong but not why. With drill-down, every metric is a doorway into the specific failure. Teams using Hamming's incident response runbook connect dashboard alerts directly to diagnostic workflows.
How to Use Production Call Replay for Voice Agent Debugging
Production call replay is the bridge between detecting a failure and fixing it. Synthetic test cases—no matter how comprehensive—cannot replicate the acoustic variability, conversational patterns, and edge cases that real users produce.
Converting Failed Calls into Test Cases
Every production failure is a free test case. The workflow:
- Identify the failure — Dashboard alert, user complaint, or QA review flags a problematic call
- Capture the evidence — Audio recording, STT transcription, expected intent, actual behavior, and full turn-level trace
- Create the regression test — Package the audio, expected outcomes, and pass/fail criteria into a replayable test case
- Add to the test suite — The test case runs automatically against every future agent version
This one-click conversion from production failure to regression test is what prevents the same bug from recurring. See Voice Agent Troubleshooting: Complete Diagnostic Checklist for the full triage-to-test workflow.
Replaying Calls Against Updated Agent Versions
Before deploying a fix, replay the exact production calls that triggered the failure against the updated agent version. This validates that:
- The specific failure is resolved
- The fix does not introduce regressions on similar calls
- The agent handles the acoustic conditions (noise, accent, speaking speed) that caused the original issue
Synthetic tests cannot replicate these conditions. A user who speaks rapidly with a regional accent in a noisy car is not something you can simulate with clean studio audio.
Automating Regression Testing from Production Data
The continuous improvement loop:
- Monitor — Dashboard detects rising failure rates or new failure patterns
- Capture — Affected calls are automatically tagged and queued for review
- Triage — QA team reviews, confirms failures, and creates test cases
- Test — New test cases run against the current and candidate agent versions
- Deploy — Fix ships only after passing all regression tests including the new cases
- Verify — Production metrics confirm the fix resolved the issue
Teams that automate this loop see compound improvements: each production failure makes the test suite stronger, which catches more issues before deployment, which reduces production failures. For implementation patterns, see Post-Call Analytics for Voice Agents.
How to Set Up Alerting and Health Checks for Voice Agents
Proactive alerting catches degradation before users complain. The goal is zero-surprise incidents: every production issue should trigger an alert before it appears in customer feedback.
Anomaly Detection Rules for Voice Agent Monitoring
Configure alerts on these thresholds as a starting point, then tune based on your baseline:
| Metric | Warning Threshold | Critical Threshold | Alert Channel |
|---|---|---|---|
| P90 Latency | greater than 2.5s | greater than 3.5s | Slack + PagerDuty |
| Task Success Rate | less than 85% | less than 80% | Slack + PagerDuty |
| WER | greater than 5% | greater than 8% | Slack |
| Intent Accuracy | less than 95% | less than 92% | Slack + PagerDuty |
| Fallback Rate | greater than 10% | greater than 15% | Slack |
| Escalation Rate | greater than 20% | greater than 30% | PagerDuty |
| TTFW P95 | greater than 3s | greater than 5s | Slack |
Use P90/P95 percentiles, not averages. Averages mask the tail latency that ruins user experience. A 300ms average latency with a P95 of 5s means 1 in 20 users gets a terrible experience.
Proactive Monitoring with Golden Call Sets
Golden call sets are curated test calls that exercise your agent's critical paths. Replay them automatically every 5-15 minutes against your production environment:
- Happy path calls — Standard flows for your top 5 intents
- Edge case calls — Known difficult scenarios (accents, noise, interruptions)
- Regression calls — Previous production failures converted to test cases
- Compliance calls — Calls that verify policy adherence and guardrail enforcement
When a golden call fails, you know something changed—whether it is a model update, infrastructure issue, prompt regression, or provider degradation. This catches problems during low-traffic periods when statistical alerts might not fire.
For framework-specific implementation, see LiveKit Agent Monitoring: Prometheus, Grafana and Alerts.
Integrating Alerts with Team Communication
Push alerts to Slack (or your team's communication tool) with enough context to act immediately:
- Affected agent name and version
- Timestamp and duration of the anomaly
- Specific metric that triggered the alert with current value vs. threshold
- Sample affected calls — Links to 2-3 representative call traces
- Drill-down link — Direct link to the dashboard filtered to the relevant time window
Alerts without context create noise. Alerts with context enable action.
Voice Quality Metrics: WER, MOS, and Task Success Rate
Three metrics form the foundation of voice agent quality measurement. Each captures a different dimension: transcription accuracy, synthesis quality, and business outcomes.
Word Error Rate (WER) and Transcription Accuracy
WER quantifies how accurately the STT engine transcribes user speech:
WER = (Substitutions + Deletions + Insertions) / Total Words × 100| WER Range | Quality Level | Typical Cause |
|---|---|---|
| less than 3% | Excellent | Clean audio, common vocabulary |
| 3-5% | Good | Minor noise, some uncommon terms |
| 5-8% | Acceptable | Moderate noise, accents, or domain jargon |
| 8-12% | Poor | High noise, strong accents, or ASR model mismatch |
| greater than 12% | Critical | Systemic audio or model issues |
Target less than 5% WER for production voice agents. Above this threshold, downstream intent classification degrades rapidly due to the multiplicative error effect.
For comprehensive WER evaluation methodology, see ASR Accuracy Evaluation for Voice Agents.
Mean Opinion Score (MOS) for TTS Quality
MOS rates the naturalness and clarity of synthesized speech on a 1-5 scale. Modern neural TTS systems achieve scores that approach human speech quality:
| MOS Range | Quality Level | User Perception |
|---|---|---|
| 4.3-4.5 | Excellent | Nearly indistinguishable from human speech |
| 4.0-4.3 | Good | Natural-sounding with minor artifacts |
| 3.5-4.0 | Acceptable | Clearly synthetic but understandable |
| less than 3.5 | Poor | Robotic, distracting, reduces trust |
MOS scores of 4.3-4.5 indicate TTS quality rivaling human speech naturalness and clarity. Below 3.5, users start disengaging regardless of how accurate the agent's responses are.
Task Success Rate and First Call Resolution
Task success rate measures whether the agent achieves the business goal of the call—not just whether it understood the words:
Task Success Rate = (Calls completing business goal / Total calls) × 100Target 85%+ first call resolution as the primary business outcome metric. Technical accuracy (WER, intent accuracy) matters only insofar as it drives task completion. An agent with 99% WER but 60% task completion has a workflow problem, not a speech recognition problem.
How to Monitor Compliance and Safety in Voice Agents
Production voice agents handle sensitive data and operate under regulatory constraints. Debugging must include compliance and safety monitoring alongside technical performance.
Detecting Hallucinations in Voice Responses
In voice agent context, hallucination detection focuses on the STT-to-response pipeline. Five or more consecutive insertions, substitutions, or deletions in the STT output constitute a hallucination event—the agent "heard" words that were not spoken and may act on phantom information.
Beyond STT hallucinations, LLM-generated responses can contain fabricated information: nonexistent policies, incorrect prices, or hallucinated appointment times. Monitor for:
- Factual consistency — Does the response match the knowledge base?
- Policy adherence — Does the response follow defined guardrails?
- Data accuracy — Are account numbers, dates, and amounts correct?
Policy Adherence and Guardrail Validation
Audit every call against your compliance rules automatically:
- HIPAA: Verify the agent does not disclose Protected Health Information without proper authentication
- PCI DSS: Confirm credit card numbers are not stored or repeated in logs
- Custom policies: Validate the agent follows your specific business rules (e.g., no unsolicited upselling, required disclosures)
For a detailed treatment of compliance frameworks, see AI Voice Agent Compliance and Security.
Prompt Injection and Safety Violations
Monitor for adversarial inputs that attempt to manipulate agent behavior:
- Users instructing the agent to ignore its system prompt
- Requests to reveal internal configuration or training data
- Social engineering attempts to bypass authentication steps
- Attempts to trigger the agent into making unauthorized commitments
Log these events, block the unsafe behavior, and feed the attempts into your test suite as adversarial regression tests. See An Introduction to Voice Agent Guardrails for guardrail implementation patterns.
Best Practices for Production Voice Agent Debugging
Effective debugging requires a systematic approach that balances technical depth with business impact.
Balancing Technical and Business Metrics
Track both sides in parallel:
| Technical Metrics | Business Metrics |
|---|---|
| ASR accuracy / WER | Customer satisfaction (CSAT) |
| Intent classification accuracy | First call resolution (FCR) |
| End-to-end latency (P50/P90/P95) | Cost per resolution |
| Fallback and escalation rates | Containment rate |
| Confidence score distributions | Net Promoter Score (NPS) |
Technical metrics explain why something failed. Business metrics tell you whether it matters. A 2% drop in intent accuracy on a low-volume, low-value intent category is not worth the same urgency as a 2% drop on your primary revenue-generating flow.
Continuous Improvement Loops
The debugging workflow is not linear—it is a cycle:
- Detect — Dashboards and alerts surface anomalies
- Diagnose — Turn-level traces and audio replay pinpoint root cause
- Fix — Update the model, prompt, configuration, or infrastructure
- Test — Replay production failures against the fix, run full regression suite
- Deploy — Ship only after all tests pass
- Monitor — Verify the fix holds in production, watch for new patterns
- Learn — Add new failure patterns to the test suite, update alert thresholds
Each cycle makes the system more resilient. Teams running this loop consistently see compound quality improvements over weeks and months—not because any single fix is transformative, but because the system learns from every failure.
How Teams Implement Debugging Workflows with Hamming
Hamming provides the tooling to operationalize the debugging practices described in this guide:
- Turn-level tracing with audio attachments for every production call
- One-click failure-to-test conversion from any call trace to a regression test case
- Production call replay against updated agent versions before deployment
- Real-time dashboards with drill-down from metrics to individual call traces
- Golden call monitoring with automated replay and alerting
- Confidence score tracking with configurable fallback thresholds
- Compliance auditing with automated policy adherence checks
- Intent accuracy breakdown by category with trend analysis
- Slack integration for contextual alerts with direct links to affected calls
