> ## Documentation Index
> Fetch the complete documentation index at: https://docs.poly.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Performance monitoring

> Track latency, containment, and ASR accuracy – use dashboards and Smart Analyst for deep insights.

Monitor key metrics like response latency, containment rate, and ASR accuracy. Use dashboards for overview data and Smart Analyst for deep sampling across 500+ conversations to identify root causes of performance issues.

<Tip>
  Use [Smart Analyst](/smart-analyst/introduction) to investigate performance issues at scale. Its **deep sampling** analyzes up to 500 conversations per query, helping you quickly identify root causes behind metrics like high handoff rates, low containment, or latency spikes – without manually reviewing individual calls.
</Tip>

## Quick reference

| I need to...              | Where to go                                                             |
| ------------------------- | ----------------------------------------------------------------------- |
| Check overall performance | **Configure > Dashboards**                                              |
| Find slow responses       | **Analytics > Conversations > Voice** → filter by latency               |
| Debug ASR accuracy        | **Analytics > Conversations > Voice** → Diagnosis → check transcription |
| Find knowledge gaps       | **Configure > Dashboards** → unhandled queries                          |
| Check function errors     | **Analytics > Conversations > Voice** → filter by errors                |
| Track version performance | Compare versions in **Deployments > Environments**                      |

## Key metrics

### Response latency

Time from when the user stops speaking to when the agent starts responding. Target: under 2 seconds.

**Common causes of high latency:** slow function execution, external API delays, complex knowledge retrieval, overly complex prompts.

### Containment rate

Percentage of calls handled without [human handoff](/call-handoff/introduction). Target varies by use case (typically 60-90%). The inverse — handoff rate — is the primary metric for the [human-in-the-loop](/glossary/introduction#hitl-human-in-the-loop) portion of the agent's traffic.

**Common causes of low containment:** knowledge gaps, complex queries, caller preference for humans, technical errors.

### ASR accuracy

How accurately the agent transcribes what the caller says. Target: above 95% word accuracy.

**Common causes of low accuracy:** background noise, strong accents, uncommon words or jargon, poor phone connection.

## Monitoring tools

### Dashboards

Go to **Configure > Dashboards** for high-level metrics: call volume, average duration, handoff rate, top intents, and performance trends. Filter by date range, environment, variant, or version.

### Conversation Review

Go to **Analytics > Conversations > Voice** to drill into individual calls. Search, filter, listen to recordings, review transcriptions, and toggle layers in the **Diagnosis** toggle group on the Transcription tab for technical details (function logs, knowledge retrieval, LLM prompts, timing breakdown).

### Smart Analyst

Use [Smart Analyst](/smart-analyst/introduction) for deeper investigation. Its **deep sampling** capability analyzes up to 500 conversations per query, surfacing patterns across your data that would take hours to find manually. Launch it directly from dashboard charts using the **Generate insights** button, or go to **Analytics > Smart Analyst** and ask questions like *"Why are calls failing containment this week?"* or *"What do low-PolyScore calls have in common?"*

### Test sets

Use [test sets](/analytics/test-suite/introduction) for automated regression testing and version comparison. Run them before promoting versions.

## Diagnosing common issues

### High latency

1. Filter **Analytics > Conversations > Voice** by high latency
2. Open **Diagnosis** → check the timing breakdown
3. Identify the bottleneck: function execution, knowledge retrieval, LLM generation, or TTS

**Fixes:** optimize slow functions, cache common audio phrases, switch to a faster TTS provider, simplify knowledge sources, add [delay controls](/tools/delay-control).

### Low ASR accuracy

1. Review transcriptions in Conversation Review
2. Compare to audio recordings
3. Look for patterns (specific words, accents, noise)

**Fixes:** add custom vocabulary, adjust ASR sensitivity, add clarification prompts for ambiguous input.

### Knowledge gaps

1. Check **Configure > Dashboards** → unhandled queries
2. Review common questions without answers in Conversation Review
3. Use [Smart Analyst](/smart-analyst/introduction) deep sampling to identify gaps at scale – try: *"What questions are we not handling well?"* or *"Where does the agent give incorrect or incomplete answers?"*

**Fixes:** add missing topics to [Managed Topics](/managed-topics/introduction), add [Connected Knowledge](/connected-knowledge/introduction) sources, improve topic descriptions for better retrieval.

### Function errors

1. Filter **Analytics > Conversations > Voice** by errors
2. Review **Diagnosis** → function logs

**Fixes:** fix code, update API credentials, add error handling and retries, add logging with `conv.log` for better debugging.

### High handoff rate

1. Check **Configure > Dashboards** → handoff metrics
2. Review handoff reasons for patterns
3. Ask [Smart Analyst](/smart-analyst/introduction): *"What are the top 5 reasons conversations are transferred to a human agent?"* – deep sampling gives you a percentage breakdown across hundreds of calls

**Fixes:** add knowledge for common handoff reasons, adjust handoff rules, add self-service options before handoff.

## Optimization quick wins

| Area            | Quick wins                                                                                      |
| --------------- | ----------------------------------------------------------------------------------------------- |
| **Latency**     | Cache common audio, switch to Cartesia TTS, use Turbo interaction mode, optimize slow functions |
| **ASR**         | Add custom vocabulary, use clarification prompts, enable noise cancellation                     |
| **Containment** | Add missing knowledge, improve handoff rules, clarify agent capabilities upfront                |
| **Quality**     | Fix pronunciations, improve response clarity, test with real users                              |

## Debugging toolkit

All debugging tools available in Agent Studio:

| Tool                                                                     | Purpose                                                                    | Where to find it                                                                      |
| ------------------------------------------------------------------------ | -------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| [Diagnosis layers](/analytics/conversations/diagnosis)                   | Inspect tool calls, knowledge retrieval, LLM prompts, and latency per turn | **Analytics > Conversations > Voice** → select a call → Transcription tab → Diagnosis |
| [`conv.log`](/tools/classes/conv-log)                                    | Add structured logging (info, warning, error) from Python functions        | Function code → appears in Diagnosis                                                  |
| [`conv.log_api_response()`](/tools/classes/conv-object#log_api_response) | Log full HTTP responses from API integrations for debugging                | Function code → appears in Diagnosis                                                  |
| [Test sets](/analytics/test-suite/introduction)                          | Automated regression testing across versions                               | **Analytics > Test suite**                                                            |
| [Alerts API](/api-reference/alerts/introduction)                         | Automated alerts for latency, errors, and call volume anomalies            | API configuration                                                                     |
| [Smart Analyst](/smart-analyst/introduction)                             | AI-powered analysis across up to 500 conversations                         | **Analytics > Smart Analyst**                                                         |
| [Dashboards](/analytics/dashboards/introduction)                         | High-level metrics: call volume, latency, handoff rates, containment       | **Configure > Dashboards**                                                            |

## Related pages

* [Smart Analyst](/smart-analyst/introduction) – AI-powered conversation analysis with deep sampling
* [Health checks](/learn/maintain/health-checks) – proactive monitoring routines
* [QA and analytics](/learn/maintain/qa-analytics) – daily dashboard and conversation review workflows
* [Function maintenance](/learn/maintain/tool-maintenance) – debugging and optimizing functions
* [Alerts API](/api-reference/alerts/introduction) – automated alerts for latency, errors, and call volume