Posts tagged with "Voice Agent Testing"

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.

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

Vapi vs ElevenLabs solve different jobs, and neither tests itself. Here are the key differences in 2026, plus where a QA layer fits in your voice stack.

Best practices for AI voice agent testing, with honest tradeoffs: 8 rules that catch the failures real callers trigger before they reach production.

Learn how to test your voice agent after building it, across six stages from component checks to production monitoring, with a clear pass bar for each.

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.

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.

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

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.

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.

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.