Posts tagged with "Monitoring"

Call analytics for voice agents should tell you why calls fail, not just how many. See how Cekura Insights clusters failing calls into a few root-cause fixes.

Pipecat testing with Cekura: run voice agent simulations, add session tracing, and monitor production performance. Catch latency and interruption issues before they reach users.

Discover why Cekura provides superior monitoring capabilities compared to traditional tracing platforms for conversational AI agents.

How to monitor AI chat and voice agents in production using Cekura’s quality metrics, dashboards, and smart alerting.

Explore 12 key metrics—like interruptions, WPM, sentiment, and talk ratio—to enhance your AI conversation monitoring and insights.

Discover the 6 most important metrics for monitoring AI conversations—Instruction Following, Latency, Hallucination Rate, CSAT, Interruption Handling, and Voice Clarity—to ensure reliable, high-performing voice and chat agents.

How to monitor live AI chat agents in production: the conversation-layer signals, the metric stack, alerting, and the remediation loop. A what-to-monitor guide from Cekura.

Automated recurring voice agent tests run your regression suite on a schedule and in CI/CD so prompt changes, model swaps, and silent drift get caught before customers do. Here is how to set them up with cron and GitHub Actions in Cekura.

Call transcript QA for a voice bot scores every call's transcript against your rules. How to review voice agent transcripts at scale with Cekura.

Retell AI voice automation features in 2026, broken down across building, voice, telephony, monitoring, and pricing, and the limits that shape deployments.

We tested 7 AI voice agent testing tools across CI/CD regression, production monitoring, and telephony-layer failures. See which ones actually passed.

I tested Twilio vs Vapi across deployment, billing, and production QA — one comparison turned into three tools solving three different problems.

Agent performance monitoring shows where AI agents fail across workflows, quality, CX, and voice. See what metrics to track before launch and after release.

Learn exactly what voice observability is, why it matters for production voice AI agents, and how to trace failures across infrastructure, execution, and UX.

Monitor Retell AI voice agents in production with latency tracking, interruption detection, tool-call monitoring, and conversational quality analysis.

Explore top voice agent monitoring platforms for tracking latency, interruptions, tool calls, and instruction adherence in production.

Explore the best chat testing platforms for reliable AI agents. Cekura offers automated QA, regression testing, and real-time monitoring to ensure seamless, scalable, and high-quality chat interactions.