Teams deploying voice agents on Retell AI need continuous visibility into how calls perform, how conversations unfold, and where failures occur. Effective monitoring must cover both operational reliability and conversational outcomes.
Cekura provides monitoring infrastructure designed for Retell-based voice agents, ingesting call data and transcripts to evaluate operational metrics, conversation behavior, and business outcomes across production traffic.
Retell AI voice agents orchestrate speech recognition, language models, backend tool execution, and speech synthesis inside a real-time conversation loop. Monitoring these systems requires visibility into latency between components, interruption handling, and tool-call execution during live calls.
Monitoring Retell Voice Agent Performance
Retell provides operational analytics such as call success rate, call volume trends, and latency. Monitoring platforms must ingest these signals and extend them with deeper analysis and reporting.
Cekura connects to Retell AI voice agents and evaluates production calls across operational metrics including:
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Call success vs failure outcomes
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Call volume trends over time
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Latency distributions including mean, P50, and P90 response latency
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Call transfers from AI to human agents
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Call completion vs early termination
These metrics allow teams to track whether a Retell AI voice agent consistently resolves tasks or drops calls prematurely.
Production calls are processed immediately after completion, and results appear in Cekura monitoring dashboards as soon as the call finishes.
Alerting on Retell AI Voice Agent Failures
Operational monitoring requires automated detection when a voice system begins degrading.
Cekura supports metric-based alerts tied to Retell AI production call evaluations. Teams can define success criteria and thresholds, then trigger notifications when metrics fall outside expected ranges.
Alerting options include:
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Slack notifications
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Email alerts
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Threshold-based triggers
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Trend-based anomaly alerts detecting spikes or drops in metrics
This allows teams running Retell voice agents to detect issues such as rising call failures, latency spikes, or abnormal call volumes without manually reviewing recordings.
Monitoring Conversational Quality in Retell Calls
Retell AI voice agents require monitoring beyond uptime metrics. Conversation quality directly affects whether users complete tasks.
Cekura evaluates Retell AI production conversations using a library of conversational metrics, including:
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Latency between turns
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Interruptions and barge-in handling
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Response consistency across turns
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Relevance and instruction adherence
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Speech clarity and pronunciation accuracy
More than 25 predefined conversational metrics are available for Retell AI voice agent analysis.
Tracking Intent Outcomes and Resolution Rates
Retell AI voice agent monitoring must connect conversation behavior to task completion.
Cekura enables teams to evaluate whether Retell AI production calls resolve the user’s request by defining expected outcomes and success criteria for each scenario.
Outcome evaluation automatically flags Retell AI production calls where a required step is missed.
This produces outcome-level metrics such as task completion success rate, handoff rate to human agents, and call containment by AI.
These indicators allow teams to measure how well Retell AI voice agents actually perform their intended tasks.
Monitoring Conversation Flow and Latency
Natural Retell AI voice agent conversation requires low response delay and smooth turn transitions.
Cekura tracks latency and conversation flow metrics across Retell AI production calls, including:
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Response latency in milliseconds
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Stop time after interruption
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Words-per-minute speaking rate
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Talk ratio between user and agent
For example, teams using Cekura monitor whether agents respond within conversational latency ranges and whether the AI dominates the conversation excessively.
These metrics help detect issues such as slow speech generation, overlapping speech after interruptions, and unnatural pacing in dialogue.
Transcript Analysis of Retell Calls
Retell AI captures recordings and transcripts for each call. Monitoring tools must analyze these artifacts to identify failures automatically.
Cekura ingests call transcripts and applies evaluation metrics to detect:
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Misunderstood intents
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Incorrect answers
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Hallucinated responses
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Inconsistent answers across turns
When issues are detected, Cekura provides timestamps linked to the transcript, allowing engineers to review the exact segment of conversation where the failure occurred.
This removes the need to manually review large numbers of Retell AI production call recordings. Learn more about how Cekura performs intent and accuracy testing for voice agents.
Continuous QA Across Production Calls
Teams typically combine monitoring with automated testing to detect regressions before deployment. See our guide on testing Retell AI voice agents for automated QA and red teaming.
Monitoring Retell AI voice agents requires evaluating production calls at scale rather than sampling a few recordings.
Cekura processes Retell AI production conversations and aggregates results into dashboards showing:
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Overall call success rate
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Performance trends across time windows
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Metric-level breakdowns for each conversation
Custom dashboards allow teams to filter by agent version, metric type, or metadata such as integration type or workflow category.
Trend charts allow engineers to detect gradual regressions in conversational behavior or infrastructure performance.
Integration with Voice AI Infrastructure
Retell AI voice agent monitoring platforms must integrate with voice agent infrastructure to ingest production call data automatically.
Cekura integrates with Voice AI platforms including:
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Retell AI
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Vapi
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ElevenLabs
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Synthflow AI
Through these integrations, production call transcripts and metadata can be sent directly to the monitoring system for analysis and reporting.
Security and Privacy Controls for Voice Monitoring
Retell calls often contain sensitive data such as personal identifiers or medical information.
Cekura includes observability safeguards designed for enterprise environments:
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Automatic redaction of sensitive data in transcripts and audio
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Role-based access control
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Compliance support including GDPR and HIPAA readiness
These controls allow teams to monitor voice conversations without exposing protected data.
Production Monitoring for Real Voice Traffic
Organizations using voice agents across customer support, healthcare operations, and business automation rely on production monitoring to maintain reliability.
Teams deploying AI voice agents such as those at Confido Health use monitoring and automated evaluation to track workflow accuracy, call latency, and backend tool-call success across large call volumes.
Monitoring results help teams verify that voice agents continue performing correctly as infrastructure, prompts, and backend systems evolve.
Continuous Visibility into Retell AI Voice Agents
Monitoring Retell AI voice agents requires visibility across several layers:
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Operational metrics such as call success, latency, and call volume
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Conversational quality signals such as interruptions, relevance, and response consistency
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Outcome metrics showing whether calls successfully complete tasks
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Transcript-level analysis identifying conversation failures
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Automated alerts when performance metrics degrade
Cekura provides monitoring infrastructure that ingests production call data from Retell AI voice agents and analyzes conversations across operational, conversational, and outcome metrics. This allows teams running voice agents in production to continuously evaluate performance and detect failures as they occur.