Monitor LiveKit voice agents across latency, turn-taking, tool calls, transcripts, audio quality, session reliability, dashboards, and production alerts.
LiveKit agents run on a real-time voice stack: WebRTC transport, streaming audio, speech-to-text, LLM reasoning, tool calls, and text-to-speech. Monitoring LiveKit voice agents means tracking the full production conversation, not just whether the service is online.
A useful LiveKit monitoring setup should show latency, turn-taking, silence, interruptions, tool execution, reasoning failures, transcript quality, voice delivery, and session-level reliability across real conversations.
Cekura helps teams monitor LiveKit voice agents by capturing production conversation data through tracing, then evaluating sessions with built-in and custom metrics. For LiveKit agents, Cekura tracing can capture audio recordings, full transcripts, LLM interaction traces, tool call requests and responses, and session metadata once a session finishes.
LiveKit voice agents are not simple request-response apps. A single conversation depends on several layers working together:
When one layer fails, the user experiences it as a broken conversation. That is why LiveKit agent monitoring needs to cover both technical performance and conversation behavior.
LiveKit teams should monitor transport health with WebRTC-level data such as packet loss, jitter, reconnects, dropped sessions, and session duration. These signals matter because voice agents are sensitive to delay, stalls, and reconnection behavior.
Cekura complements LiveKit and WebRTC-level telemetry by evaluating the user-visible effects of transport and infrastructure issues after the session is processed. These include:
Cekura’s standard conversational metrics include latency, overall silence failure, main-agent silence failure, AI interruption, user interruption, and interruption overrun.
Cekura’s standard conversational metrics include latency, overall silence failure, main-agent silence failure, AI interruption, user interruption, and interruption overrun.
For teams that need raw RTP stats, jitter charts, packet loss, or SFU-level diagnostics, Cekura should sit alongside LiveKit-native or WebRTC-level monitoring. Cekura’s strength is turning production conversations into session-level and cross-session quality signals.
Latency is one of the most important metrics for LiveKit voice agents. Delays can come from transport, STT, the LLM, tool calls, TTS, or orchestration logic. A LiveKit monitoring tool should track:
Cekura tracks latency and allows teams to define custom success or failure criteria around latency. For example, teams can mark a call or metric as failed when average latency or peak latency crosses a configured threshold.
Cekura can also support custom latency metrics through Python code metrics, using structured transcript data, metadata, dynamic variables, call duration, and other call fields.
A LiveKit agent usually combines multiple providers and components:
Monitoring has to show more than one global success score. It should help teams identify whether a failure came from transcription, reasoning, tool execution, voice delivery, or orchestration.
Cekura evaluates pipeline outcomes using metrics such as:
Cekura tracing can capture LLM interaction traces, tool call requests and responses, transcripts, audio recordings, and metadata from LiveKit sessions.
For stage-specific metrics, teams can send timestamps, provider metadata, tool events, and transcript JSON into Cekura, then define custom metrics over that data. This supports monitoring for metrics such as time to first response, transcription delay, tool latency, workflow completion, and compliance checks.
Turn-taking is one of the hardest parts of production voice AI. A LiveKit agent can have a strong prompt and still fail if it talks over users, misses interruptions, pauses too long, or resumes at the wrong moment.
A monitoring setup should capture:
Cekura tracks both user interrupting AI and AI interrupting user, with stereo recordings recommended for interruption analysis. It also tracks talk ratio, words per minute, latency, silence failures, repetition, and termination behavior.
This matters for LiveKit agents because many production failures are temporal. Logs alone are often not enough. Teams need timestamps, transcripts, and audio context to understand exactly when the conversation broke.
Most LiveKit agent failures are not pure infrastructure failures. Many come from reasoning, workflow, tool usage, memory, or context handling.
A monitoring tool should capture:
Cekura supports tool-call checks by analyzing tool call results alongside transcripts. Teams can also pass metadata into Cekura and write custom Python metrics over that metadata.
Cekura’s instruction-following metric identifies deviations from the agent’s instructions and categorizes issues by type, scenario, and priority. This helps teams find production failures they did not explicitly define as metrics beforehand.
LiveKit voice bugs are easier to diagnose when the session can be replayed with its transcript, audio, timestamps, and trace data. A useful monitoring setup should include:
Cekura tracing captures audio recordings, full transcripts, LLM interaction traces, tool call requests and responses, and session metadata. When a LiveKit session finishes, that conversation data becomes available in Cekura for observability and analysis.
Cekura also provides timestamps for metric failures and successes, which helps teams locate where a conversation went wrong.
Voice quality affects the entire LiveKit agent experience. If the agent speaks too quickly, has poor clarity, mispronounces key terms, or sounds unnatural for the context, the conversation can fail even when the logic is correct. A monitoring setup should track:
Cekura includes standard voice and conversation metrics such as WPM, talk ratio, average pitch, voice tone and clarity, pronunciation checks, CSAT, and sentiment.
Cekura supports metric coverage across speech quality, conversational flow, accuracy and logic, and customer experience, including voice clarity, pronunciation, silences, interruptions, hallucinations, transcription accuracy, relevancy, CSAT, sentiment, and drop-off points.
Production LiveKit agents need reliability monitoring beyond uptime. The service may be online while the agent is still failing conversations. A LiveKit monitoring tool should detect:
Cekura monitors infrastructure and conversation-level failures such as latency, silence, interruptions, tool call success, repetition, and termination behavior.
Cekura can also classify and prioritize production issues. In observability workflows, teams can see deviations from instructions, mark issue priority, and receive issue summaries with frequency so they can focus on the most common or highest-impact failures.
Monitoring LiveKit agents at scale requires dashboards and alerts that summarize performance across many sessions. A useful dashboard should support:
Cekura supports observability dashboards, metric-wise performance, call analysis, and production call alerts.
Cekura also supports custom dashboards, metric plots, group-by filters, and trend-based alerts. Trend-based alerts notify teams when metrics drift from normal patterns rather than relying only on fixed thresholds.
Cekura’s production monitoring is post-call. Production calls are analyzed after the call completes, and dashboard results update once processing is complete.
Individual session debugging works during development. It breaks once a LiveKit agent handles hundreds or thousands of production conversations. At scale, teams need to monitor:
Cekura is designed to aggregate conversation analysis across many calls, so teams can identify patterns instead of manually listening to recordings one by one. The Cekura monitoring launch specifically frames the problem as teams spending dozens of hours manually listening to thousands of calls before moving to automated monitoring.
For higher-volume workloads, Cekura supports custom concurrent calls on enterprise plans and load testing as a service.
Voice agents have tight per-minute economics. Monitoring should help teams understand not only whether conversations succeed, but also what it costs to evaluate production quality. For LiveKit monitoring, teams may want to track:
Cekura uses credits across testing, monitoring, and evaluation. For monitoring and observability, evaluation costs are based on metric runs. Example: importing an external call and running 10 metrics costs 2 credits, based on 0.2 credits per metric run.
For provider-specific cost observability, teams should pass provider, model, and infrastructure metadata into Cekura so dashboards and custom metrics can segment results by configuration.
Monitoring LiveKit agents is not the same as standard application performance monitoring. A useful setup needs three layers at once:
Cekura offers the strongest support for the second and third layers, with application-level signals for the first layer. Teams that need raw WebRTC packet-level telemetry should pair Cekura with LiveKit-native or WebRTC-level metrics.
Cekura helps teams monitor LiveKit voice agents by turning production conversations into structured metrics, dashboards, alerts, and traceable failure reports.
For LiveKit agents, Cekura can:
Cekura’s LiveKit tracing workflow is built around the full lifecycle of production voice agents: capture the conversation, evaluate it, identify the failure, and track whether fixes improve future sessions.
| Must-have | High-value | Advanced |
|---|---|---|
| End-to-end latency tracing | Turn-taking metrics | Raw WebRTC telemetry |
| Session replay with audio and transcript | Silence and interruption detection | RTP stats, jitter, and packet loss |
| Conversation-level metrics | LLM trace inspection | Real-time in-call debugging |
| Tool call success and failure tracking | Custom metrics | Stage-level cost attribution |
| Failure timestamps | Python or code-based metric logic | Multi-agent graph visualization |
| Dashboards for production conversations | Historical re-evaluation | Deterministic replay |
| Alerts for quality or reliability degradation | Issue frequency and severity tracking | Chaos testing hooks |
| Metadata filtering by agent, version, or environment | Provider or model comparison through tags and metadata |
Cekura covers the core monitoring workflow for LiveKit voice agents at the conversation, trace, metric, and dashboard layer. For teams that need packet-level WebRTC telemetry or live in-call debugging, Cekura should be paired with LiveKit-native observability and infrastructure monitoring.
Test LiveKit voice agents with automated QA, scenario testing, and regression testing across realtime interactions, STT LLM TTS pipelines, multi-turn conversations, and tool calls.