TestMu AI Blogs

Kore.ai's Batch and Conversation Testing score NLU accuracy and flow coverage, not generative-answer quality at scale. Here's how to fully test a Kore.ai bot.

Haptik's Test Bot and debug logs check one conversation at a time. They don't score Contakt's generative answers at scale. Here's how to test a Haptik chatbot.

Parloa's Simulations score synthetic callers, not real telephony audio. Here's how to test a Parloa voice agent with real calls, accents, and noise.

Dialogflow, Lex, and Watson each ship a different native testing tool, none scoring conversation quality at scale. Here's how to test all three the same way.

Testing a Copilot Studio agent means running it through three layers: Microsoft's built-in Agent Evaluation feature for pass or fail scoring against test sets, the Power CAT Copilot Agent Kit for batch tests and CI/CD pipelines, and a platform such as TestMu AI's Agent Testing that scores hundreds of persona-varied conversations against quality metrics before real users reach it.

Vertex AI's Gen AI evaluation service scores final response and trajectory against your references, not real-user traffic. Here's how to test one at scale.

LangSmith and AgentEvals test your LangGraph agent's code and trajectories. Neither sweeps hundreds of real-user conversations at scale. Here's how to test one.

Testing an Amazon Connect bot means validating the Amazon Lex V2 intent recognition, AWS Lambda fulfillment, and Amazon Q in Connect generative answers that make up its contact flow. Amazon's Lex Test Workbench checks accuracy before launch, while Contact Lens only analyzes real calls afterward, leaving automated evaluation across personas, accents, and noise to catch what neither tool covers.

Compare the 9 best AI agent evaluation tools and platforms for 2026, from open-source frameworks to autonomous agent testing, with features and the right fit.

Auditing a login-gated or SSO-protected app for accessibility means scanning the authenticated session itself, not the sign-in screen an anonymous request receives, since a URL-only scanner never gets past the login form or its SSO redirect. TestMu AI's Accessibility MCP Server audits that signed-in session instead, reached through a tunnel or an already-authenticated automated test.

Compare the 9 best agentic coding CLI tools for 2026 on models, MCP support, open-source licensing, and CI fit, from Claude Code and Gemini CLI to Aider.

KaneAI tests desktop apps that render their interface in a browser engine, such as Electron apps built on Chromium, by describing flows like settings changes or onboarding in plain English rather than scripting WinAppDriver or Appium. It runs those steps through a secure tunnel for firewalled builds and exports the finished test to Selenium, Playwright, Cypress, or Appium code.

TestMu AI tests a chatbot by sending autonomous AI evaluators to converse with it like real users, then scoring every reply across nine quality metrics such as hallucination detection, context awareness, and tone consistency instead of matching one exact string. It also probes for prompt injection and jailbreaks, needing no code, scripts, or maintained assertions.

TestMu AI tests a voice agent by placing real phone calls through autonomous phone-caller agents and scoring the conversation across more than 30 metrics, including intent recognition, speech-to-text accuracy, containment rate, and estimated CSAT. It runs the same scenario through 200+ voice profiles, 50+ accents, and 15 background-noise presets, catching failures a single scripted call never reaches.

TestMu AI tests an IVR system by placing a real phone call, pressing the same DTMF digits a caller would, and scoring the routed path across more than 30 metrics, including DTMF Detection, Containment Rate, and First Call Resolution, instead of a telephony script that breaks when a menu is reordered. It also runs paths through accents and noise.

How to test a WhatsApp bot: sandboxes, the 24-hour window and October 2026 pricing, buttons and lists, opt-out, load, and no-code runs on TestMu AI.

TestMu AI tests AI agents by turning a plain-English description of the agent's job into 60 to 100 or more scenarios, which 15+ autonomous evaluators run and score for hallucination, bias, lost context, and broken guardrails. Each run ends in a Green, Yellow, or Red go-live verdict backed by the exact conversation turn that failed.

TestMu AI tests a Vapi voice agent by placing real phone calls to it and scoring each stage of its speech-to-text, reasoning, and text-to-speech pipeline, across more than 30 telephony metrics and nine conversation-quality metrics, instead of scripting the Vapi API by hand. It also checks Squad handoffs and tool calls, returning a Green, Yellow, or Red verdict.

TestMu AI tests a Retell agent by placing real outbound phone calls to it and scoring each conversation across more than 30 voice metrics, including intent recognition, speech-to-text accuracy, and accent handling, instead of scripting the Retell SDK by hand. Results include a confidence level and the exact transcript turn behind any failure.

The 11 best MCP servers for test automation in 2026, from Playwright MCP and Chrome DevTools MCP to Selenium, Postman, and axe-core, compared for QA teams.