Blog posts

Cekura has an MCP server. Coding agents (Claude Code, OpenAI Codex, Cursor, Windsurf) can trigger voice agent test runs, schedule recurring evals, and review pass/fail results without leaving their editor.

Learn how to build a self-improving voice agent loop that automatically diagnoses failing evals, applies prompt fixes, catches regressions, and iterates to 100% pass rate.

Developer's guide to voice AI evaluation in 2026. Metrics, scenario testing, hallucination detection, persona QA, and per-stack testing for major voice stacks.

Learn how to build voice evals that automatically improve from human feedback using Meta-Harness, reaching 95-100% human agreement in 4 to 6 iterations.

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

Learn how to build a complete scenario test suite for your voice AI agent — covering workflow tests, red teaming, knowledge base scenarios, conditional actions, and how many scenarios you actually need.

Learn how to build production-grade knowledge base connectors and implement RAG-based agentic retrieval for voice AI agents — with async syncing, SSRF protection, and observability.

Your customers don't all sound the same. Your testing shouldn't either. Discover how Cekura tests voice AI agents across 30+ languages, regional accents, and culturally authentic personalities.

In Voice AI, small changes are dangerous. Learn how to build a production-grade CI/CD pipeline with unit tests, E2E infrastructure testing, and a production feedback loop that catches failures before they reach users.

Single-turn red teaming has a 19.5% success rate. Multi-turn attacks hit 92.7%. Here's the data behind why multi-turn red teaming works and how we automated it for voice AI.

Cekura's founding Forward Development Engineer shares hard-won lessons on building reliable voice AI evaluation metrics — from avoiding cross-pollination to dynamic variable-driven testing patterns.

Learn how to test and monitor LiveKit voice agents using Cekura's tracing SDK — covering automated simulation, production observability, custom metrics, dashboards, and alerts.

Cut through the noise in the Voice AI testing space. Learn the 4 levers — Feature, Integration, AI, and Infrastructure — that separate real platforms from wrappers, and how to evaluate vendors before you commit.

Learn how Cekura's red-teaming framework tests chat and voice AI agents for bias, toxicity, and jailbreak vulnerabilities before they reach production.

Learn how Conditional Actions in Cekura enables dynamic, rule-based testing that adapts to agent responses in real-time, solving LLM hallucination and test flakiness problems.

Learn how Cekura built a custom autoscaling engine using Redis, Celery, and AWS ECS to handle unpredictable spikes, enforce multi-tenant fairness, and scale from one to hundreds of workers.

Most voice AI failures don't happen because of hallucinations or mispronunciations. They happen during silence. Learn how to engineer resilient voice AI systems that handle the milliseconds between words.

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.

Cekura lets you replay production calls against new model versions to detect regressions, benchmark performance, and validate upgrades automatically - all from real user data.

Learn why single-turn evaluation methods are insufficient for conversational AI and how multi-turn simulations provide a more accurate assessment of chatbot performance, context awareness, and conversation quality.

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.

Should you switch to GPT-5, Gemini 2.5, or DeepSeek for your Voice AI or Chat AI agents? Learn from real A/B testing, benchmarking, and regression testing insights on choosing the right LLM for Conversational AI.

Learn how silent errors in LLM-powered systems can erode performance and trust plus practical tips to catch failures early and keep your AI reliable.

Explore top TTS models that deliver authentic voices. Learn how human-like speech improves conversational AI experience and what to test in your next voice agent."

Cekura secures $2.4M in funding to power reliable QA for voice and chat AI agents—bringing AI testing and observability to the next generation of Conversational AI Agents

Explore how Cisco and Cekura are delivering seamless end-to-end AI Testing, observability for enterprise conversational AI deployments.

Dive into emerging trends and real-world applications in conversational AI: from voice AI agents in healthcare, finance, logistics and other sectors

Discover red teaming strategies that expose vulnerabilities in your Voice AI and Chat AI agents before they scale. Learn how adversarial AI testing helps create safer, more LLM agents

I tested the top conversational AI testing tools and documented what works. Best practices, honest reviews, and updated pricing, all in one place for 2026.

Helicone vs Langfuse vs Cekura aren't competing for the same users. Here are the main differences, and what's best for your voice or chat AI stack in 2026.

Find out how to write a script for AI voice training with templates, recording tips, and QA checks. Use these steps to record cleaner voice samples today.

Bad VoIP calls don't warn you until it's too late. Find out how VoIP testing exposes what's breaking your call quality before your customers hear it first.

Learn how to do a penetration test for voice AI agents across prompts, audio, tool calls, PII leaks, and regression checks before launch. Here are 8 steps.

Most teams pricing AI voice agents are guessing. Here's the 6-model breakdown with real platform costs and examples you can use today.

Voice agent performance testing goes beyond transcripts. This guide covers five methods that catch what manual reviews miss, with examples from real teams.

Braintrust pricing looks simple until overage costs kick in. I broke down every plan, real monthly costs, and where the free tier stops being enough in 2026.

Galileo AI pricing looks simple until you hit production, then issues arise. Here's what the plans actually cost you at real trace volumes in 2026.

Most AI voice assistants fail in production. Learn how to make an AI voice assistant that handles real users, noisy audio, and edge cases from day one.

Retell AI works if you have engineers on your team to spare, but it's not for everyone. Here are 8 Retell AI competitors worth trying that I tested in 2026.

Retell AI pricing per minute looks simple until you see the full bill later. Here's what each component costs and what most teams miss before they go live.

Learn automated QA best practices for voice agent testing with realistic audio, multi-turn flows, release gates, production QA, and regression checks.

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

How does voice AI work in production? It runs a live loop of transcribing, deciding, and speaking. This guide shows where it breaks and what you should test.

Vapi AI pricing starts at $0.05 per minute for calls, but what you actually pay rises once you add models, telephony, and compliance. Here's a full breakdown.

I tested 9 Vapi alternatives in 2026, so your team doesn't have to. Compare each on pricing, use cases, and trade-offs to find the right pick for your needs.

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

What voice AI works best for outbound sales calls in 2026? Compare the 7 platforms we tested, with everything you need to know on pricing and features.

Arize AI wasn't built for LLM agents, but the best Arize AI alternatives were. Here's an honest comparison of your options and who each one is built for.

Compare Retell vs. Vapi on pricing, features, latency, and compliance. I tested both platforms to help you pick the right voice AI for your team in 2026.

Most online Vapi review options stop at the feature list. This one goes further, diving into what breaks in production and what it actually costs per minute.

Arize AI pricing looks simple until you run RAG workloads or hit a compliance requirement. Here's a breakdown of the real costs and when to look elsewhere.

Choosing the best TTS for AI voice agents means considering latency, pricing, and quality under real calls. Here's what separates each provider in 2026.

Conversational analytics turns every voice call into structured data. Learn how it works, what metrics matter, and how voice AI teams use it in production.

It's easy to find an ElevenLabs alternative. What nobody tells you is which ones actually survive when real callers are on the line. Here are the top options.

LLM monitoring helps teams track quality, latency, cost, safety, and regressions in production. Here are the tools, metrics, and best practices that matter.

The best-rated AI virtual receptionist voice tools all promise 24/7 coverage. I tested nine options for 2026 to find out which ones actually deliver it.

Most voice agents fail in production. Here's how to write AI voice assistant response guidelines and voice agent prompts that hold up on real calls.

Your IVR passes pre-launch IVR testing and still breaks in production. Here's why, which methods prevent it, and the tools teams use in 2026.