Blog posts

Learn the mechanics of endpointing and turn detection in voice AI, the three signals modern agents use, and how to measure and test conversational timing at scale.

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

AI voice agent ROI calculator guide to find out how much your team can save by automating calls, with benchmarks and a step-by-step model for 2026.

Chatbot evaluation goes beyond pass/fail. Learn the 3 methods and 8 metrics engineering teams use to catch failures before production.

I compared the top Livekit alternatives for voice AI teams on pricing, latency, and telephony to find out the pros and cons and what's worth the switch.

LiveKit pricing goes from free to custom, and each tier comes with its own unique set of features. Here's what you get at each level and when to upgrade.

Voice AI for agent orchestration is a crowded category. I ran real call flows on the platforms that matter, including latency, billing, and break points.

AI voice agent accuracy testing measures how correctly an agent hears, understands, and acts on what callers say, across speech-to-text, intent and entity recognition, and end-to-end task completion. Here is how to test each layer with Cekura.

Custom KPIs for voice agent monitoring are team-defined metrics that score live calls against your own business rules. How to build and track them in Cekura.

Hallucination detection for voice AI measures whether a voice agent's spoken answers stay grounded in its knowledge base instead of inventing facts. Here is how RAG grounding checks, factuality evals, and Cekura catch hallucinations before production.

Instruction following evaluation tests whether a voice bot obeys its system prompt across a full call. How Cekura scores instruction adherence at scale.

Outbound voice AI QA is the practice of simulating, scoring, and load-testing an outbound voice agent before it runs a live calling campaign. This guide covers what to test, how to test outbound voice agents at scale, the compliance checks that matter, and where Cekura fits.

Persona-based voice AI QA tests whether a voice agent stays accurate, consistent, and on-brand across many simulated caller personas. Here is how it works, why it matters in 2026, and how Cekura runs it at scale.

Voice bot testing for fintech is the practice of simulating and scoring financial-services voice AI calls against compliance, PII redaction, and no-advice guardrails before and after launch. Here is how Cekura does it.

Compare the 9 best AI virtual receptionist voice technology tools in 2026 across workflow testing, infrastructure behavior, production monitoring, and risk.

Picking the wrong AI voice agent platform can cost you weeks. I tested each one on calls across outbound, support, and enterprise. Here's what survived.

Tested across STT, TTS, and full-stack orchestration in production. So, what is the best AI voice API for developers? It depends on your stack layer.

Not all voice AI APIs handle real-time audio equally. Testing the best voice AI API for real-time audio processing revealed bigger gaps than you'd think.

Voice AI fails when humans push it past training data. Here's how AI voice assistants process language across 4 layers and where each breaks.

LLM as a judge uses a large language model to score AI outputs at scale. Learn how it works, its pros, cons, and best practices for voice and chat agents.

Voice AI automation is replacing phone agents fast, but most deployments end up failing. Find out how the stack works, and which 7 platforms survived testing.

Learn how voice quality testing works, the methods and tools teams use, and how to test speech clarity, latency, audio quality, and real call performance.

I reviewed conversational AI platforms for voice automation and production reliability. These 8 handle testing, monitoring, and deployment best in 2026.

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.