Analyze Audio Quality Report

Turn a Coval audio-quality testing report into a practical agent-fix plan. This skill assumes the user already ran comparable Coval evaluations across audio quality scenarios, usually by using Coval personas and setting the report to Compare by Persona.

If no report or run results exist yet, ask for them. Do not invent results. If an end-to-end audio-quality workflow skill is installed in the user's environment, use that workflow first to create the runs and report, then return to this skill for analysis.

Inputs

Accept any of:

• Coval report URL or shared report URL
• report export, screenshots, or copied metric table
• Coval run IDs from the same audio-quality testing sweep
• representative simulation links, recordings, transcripts, traces, or Human Review labels

If Coval CLI/API access is available, use it to inspect runs, simulations, metrics, transcripts, recordings, and traces. If access is not available, work from the provided report/export and ask only for the smallest missing evidence needed to avoid guessing.

Workflow

1. Verify The Comparison

Confirm:

• same agent or intentional agent variant
• same test set and sampled cases across audio-quality scenarios
• same metrics across runs
• all compared runs reached terminal status
• the report is grouped by audio scenario, with Standard Customer or another clear baseline

Call out caveats before interpreting results. Common caveats: missing audio scenarios, different test cases, incomplete runs, too few simulations per scenario, changed agent config between runs, or metric definitions that do not apply to voice data.

2. Build Audio-Condition Deltas

Compare each audio-quality scenario against the baseline. Track direction and magnitude for:

Area	Typical Signals
Task outcome	composite score, task completion, scenario-specific pass/fail metrics
Responsiveness	latency, time to first audio, trace TTFB, provider response time
Speech recognition	STT WER from traces, transcription error, audio-upload WER with ground truth
Generated voice quality	Voice Quality, Speech Artifact Score, artifact-specific metrics
Conversation flow	interruption rate, silence, repetition loops, sentiment, turn timing
Call shape	turn count, audio duration, early termination, abnormally short or long calls
Scoreability	UNKNOWN, missing, failed, or unscored metric results

Use the metric's natural direction. Lower is better for latency, WER, transcription error, artifact rate, silence, and interruption problems. Higher is better for success, completion, accuracy, and quality metrics.

Do not overfit to one row. Treat one-off failures as hypotheses unless the metric, transcript, recording, or trace evidence supports the pattern.

Treat UNKNOWN, missing, failed, or unscored metric results as evidence to inspect, not as data to ignore. Under heavy audio stress, a judge may be unable to score because the call ended early, the transcript is too sparse, or the interaction became too anomalous to evaluate.

3. Inspect Representative Evidence

Open or request representative simulations:

• worst regression for each affected audio condition
• one healthy baseline simulation
• any surprising metric outlier
• any UNKNOWN, missing, failed, or unscored metric result
• very short calls, very long calls, or early terminations compared with baseline
• cases where binary task metrics passed but the transcript or recording shows a materially different experience
• Human Review disagreements, if present

For each important example, inspect the transcript and recording when available. Use traces when present to distinguish agent-side latency, tool delay, STT/TTS behavior, and turn-taking issues. Separate metric evidence from listening judgment and label anything that is inferred.

4. Diagnose By Audio Condition

Use audio-condition hypotheses, then verify them against evidence:

Audio Condition	Likely Failure Modes To Check
Standard Customer	General agent, test, metric, or tool issue if this baseline also fails
Impatient Customer	Long answers, slow tools without status, too much confirmation, poor concise recovery
Confused Customer	Prompt lacks clarification strategy, agent over-assumes, weak explanation or repair loop
Interruptive Speaker	Barge-in handling, state recovery after overlap, talking past the user, duplicated tool calls
Super Fast Speaker	STT finalization, compressed turns, missed entities, confirmation strategy, response pacing
High Background Noise Speaker	STT robustness, repeat/confirm policy, important-field validation, noise-sensitive routing
Low Volume Speaker	VAD/input gain, STT confidence, missed details, excessive reprompts, weak fallback path

If the report uses custom Coval persona labels to represent audio conditions, map each one to the closest audio-quality condition and state the mapping.

5. Recommend Fixes

Recommend specific next steps. Prefer small, testable changes over generic advice. Separate:

• Agent prompt changes: instructions for clarification, concise status, read-back, interruption recovery, or escalating uncertainty.
• Tool handling fixes: timeout handling, idempotency, retry policy, tool result grounding, duplicate-call prevention, or better user-facing status.
• STT/TTS adjustments: STT model/config, VAD thresholds, input gain, partial/final transcript handling, TTS voice/rate, or artifact mitigation.
• Trace or metric setup: add OpenTelemetry traces, trace metrics, or Human Review labels when the current evidence cannot explain the failure.
• Coverage changes: add more cases or audio conditions only when the current data shows a blind spot or the sample size is too thin.

Tie each recommendation to the audio condition, metric delta, call-shape change, scoreability issue, and representative simulation evidence that motivated it. If a recommendation cannot be tied to evidence, mark it as a hypothesis and explain how to validate it.

6. Plan The Confirmation Run

End with a small validation plan:

• which fix to try first
• which audio-quality scenario(s) and test cases to rerun
• which metrics should improve
• what regression guard should stay unchanged

Use the same baseline/report structure so the user can compare before and after.

Output Format

Return:

## Audio Quality Report Analysis

### Executive Summary
- Biggest audio-condition regression:
- Most important call-shape or scoreability anomaly:
- Most likely root cause:
- Highest-confidence next fix:

### Scope And Caveats
- Report/runs analyzed:
- Baseline:
- Missing or weak evidence:

### Audio-Condition Regression Table
| Audio Condition | Main Regression Or Anomaly | Evidence | Likely Cause | Confidence |
|-----------------|----------------------------|----------|--------------|------------|

### Representative Evidence
- Baseline simulation:
- Regression examples:
- UNKNOWN/unscored examples:
- Trace/Human Review evidence:

### Recommended Agent Fixes
1. Fix:
   - Why:
   - Exact change:
   - Validation:

### Confirmation Plan
- Rerun:
- Metrics to watch:
- Pass criteria:

Keep the final answer concise enough for a product or engineering owner to act on. Include links or IDs for report, run, simulation, trace, and Human Review evidence when available.

Guardrails

• Do not expose API keys, secrets, or private customer data.
• Do not claim traces explain a result unless trace data was available and inspected.
• Do not ignore UNKNOWN, missing, failed, or unscored metrics. Explain whether they look like measurement gaps or symptoms of an anomalous conversation.
• Do not treat generated voice quality metrics as STT accuracy metrics, or STT metrics as TTS quality metrics.
• Do not recommend changing Coval metrics or tests when the evidence points to the user's agent, unless the metric/test setup is genuinely flawed.
• Do not present "expand audio-quality coverage" as the main recommendation when the report already shows a clear fixable failure.