TestMu AI Blogs

Compare 7 AI browser testing CLIs on what each returns to CI: page state, screenshots, or a pass/fail verdict. Verified commands, real runs, and honest limits.

Compare 9 Playwright CLI alternatives, from Cypress and WebdriverIO to agentic CLIs like Kane CLI, Shortest, and Hercules. Commands, fit, and tradeoffs.

Compare 5 Playwright MCP alternatives for AI agents, from Chrome DevTools MCP to Kane CLI and Mobile MCP, with tool counts, context cost, and setup commands.

15 agentic AI testing tools that write, run and fix their own tests. Ranked on what the agent does alone, where the test code lives, and where each one breaks.

Ten agentic QA tools compared on autonomy, test ownership, coding-agent support and execution breadth, with a stated methodology and honest limits.

Agentic test management tools that write cases from requirements, refine what exists and keep traceability current. 10 platforms compared on what their AI owns.

Nilesh Dalvi of Glean on why 5x faster coding cut his cycle by only 16 percent, and what turns an AI assistant into a coworker that advances the work.

AI powered testing tools that generate cases, heal them and judge what broke. 15 platforms compared on the AI capability each brings and where the tests end up.

Harleen Bedi, CTO of Quality Engineering at Infosys, on why coverage stopped being the benchmark and who is accountable when AI writes and passes the tests.

Lisha Rakesh of Capgemini on the assisted, augmented and autonomous modes of AI in QE, why autonomous is not unsupervised, and the 80/20 shift to strategy.

Rushabh Mehta of Meta on agent evals: idempotency keys, checkpointing, memory TTLs, the three grader types, and why GAIA 2 shows temporal awareness still fails.

Magnus Hillestad of Sanity on why agents are not digital employees, what Myriad cost at $10,000 a day, and the missing compiler for organising intelligence.

Jaydeep Chakrabarty of Piramal Finance on why retrieval alone caps your AI stack, and how a knowledge graph can derive context nobody ever wrote down.

Viktoria Semaan of Databricks on running 16 models through one eval set, why Gemma 12B matched Sonnet at a fraction of the cost, and when fine-tuning pays.

Monika Sharma of Salesforce on DeepEval as pytest for LLMs, the RAG and safety metric taxonomy, choosing thresholds, and where eval tests fit in the pyramid.

A Testμ 2026 panel with CITY Furniture, Vail Resorts and TestMu AI on pooled QA teams, change failure rate, and why everyone becomes a QA architect.

Partha Sarathi Samal of Paramount on replaying 18 months of incidents against 4,200 tests, the proven, duplicate and unproven verdicts, and their limits.

Sobhitha Neelanath of Salesforce on why a 94.2% pass rate missed a $400,000 refund loop, and the trust equation quality leaders should measure instead.

Rhea Goel of Amazon on replacing a re-ranker with an LLM: natural language objectives, fine-tuning, DPO, hard and soft constraints, distillation and LLM judges.

Mesut Durukal of OMP on the ten root causes behind a four-hour pipeline nobody trusted, and the three AI skills his team now runs before any test merges.