TestMu AI Blogs

John Liang of Salesforce on four phases of agentic testing, from validating every step to running thousands of concurrent voice calls with agent-to-agent tests.

A Testμ 2026 panel from Associated Bank, Symplyphy.ai and Fénix Advisory on why AI pilots stall on trust, probabilistic quality, and earning agent autonomy.

Deepak Kamboj of Microsoft on running 14,000 tests across a 100-package monorepo, risk-based selection that cut regression feedback 60%, and agent governance.

Amanda Martin of Vapi on three restaurant booking agents built from the same script, why only one caught an ambiguous date, and how to test with simulations.

Wessel Braakman of Bouvet on five developer types every tester meets, what each one needs from a bug report, and the encounters that taught him the difference.

Gil Zilberfeld of TestinGil on golden data sets, scorecards and sanity tests, and why fixing a bug inside a prompt is only the start of fixing it.

Sai Krishna and Srinivasan Sekar of TestMu AI on testing video AI agents: lip sync, false silence, turn taking, and one scenario across many personas.

Gurleen Kaur and Keerthivasan Devaraj of Piramal Finance on going from zero to 947 business builders, 369 live apps, and why quality became a hard CI gate.

Mahesh Sathiamoorthy of Bespoke Labs on why RL environments are the scarce ingredient in agent building, with case studies from Snowflake and Credit Karma.

Four enterprise AI leaders on why generating 10x more code is the wrong goal, and what trust, determinism and outcome measurement actually take at scale.

Prince Verma of TestMu AI on why an agent sounds as confident when wrong as when right, and the six capabilities an agentic harness needs to ship safely.

Rahul Shetty on what QA engineers should learn first in the AI era, how to prove real agent experience to recruiters, and whether the SDET role is shrinking.

Nishant Gupta of Meta on the patterns that keep probabilistic AI agents safe in production: tool contracts, policy gates, budgets and replayable traces.

Francesca Lazzeri of Microsoft on why generic agent metrics miss real failures, and the four-layer evaluation loop built on ASSERT, an open-source framework.

Kishor Wikhe on why AI shifts testing from finding bugs to proving trust, and what trust engineering means for quality teams shipping AI at enterprise scale.

AI evals score AI outputs against a fixed dataset instead of asserting pass or fail. Learn the four parts of an eval, the main types, and how to gate a release.

AI-native test planning uses AI to draft cases from requirements and rank coverage by risk. Learn the workflow, the review gate, and what AI cannot decide.

Autonomous test orchestration lets an agent decide what runs, where, and what broke. See the four decisions, a working config, and which calls to keep gated.

Test apps built with Base44 end to end, from generated forms and logins to the database and access rules behind them, in plain English. No Selenium, no code.

Flowise hit end of life on August 31, 2026. Learn how to test self-hosted Flowise agents using the prediction API, CI gates and conversation quality checks.