Is "Permutation Test" sufficient for a/b testing? I read the "Practical Statistics for Data Scientists" book which is written by Peter Bruce and Andrew Bruce. In this book, the hypothesis testing is only realized with permutation test. If I searched the web, there are formulas, also I did not see too many permutation test blog articles. I want to ask that the permutation test is enough to analyzing a/b experiment data, or do I need different thing ?
Sorry for my grammatical mistakes, my native language is not English.

Ultimately permutation tests are a type of statistical significance test; we could have use bootstrapping if we want another frequentist non-parametric approach (e.g. see "How do bootstrap and permutation tests work?" (2003) by Janssen & Pauls for a relevant comparison). Whether or not they should be used instead of standard parametric tests is an question that is really old (e.g. see The large-sample power of tests based on permutations of observations (1952) by Hoeffding as an early attempt to use permutation tests instead of standard parametric tests of hypothesis testing) and doesn't really have a definitive answer. It might be even argued that permutation tests construct a null when there is not a well-defined one while parametric test never shy away from the explicit definition of a null - see Chapt. 17 Large-Scale Hypothesis Testing and FDRs in Computer Age Statistical Inference (2016) by Efron & Hastie for a more careful discussion on that.
Permutation tests therefore are not a statistical panacea. As such, think the question should not be if "permutation testing" is sufficient for A/B testing but if "hypothesis testing" is sufficient for A/B testing. In that case, the answer is no. We need regression analysis, we need to be able to be certain that there are not confounding variables that we accidentally ignored or unexpectedly included, and so forth. Yes,  permutation testing is enough in the majority of cases where everything has gone as planned, we observe no seasonality, no primacy or novelty effects, we have limited selection bias, and what not. In order cases, I would strongly urge you to educate yourself in regression analysis and general experimental design principles too. Recently, causal inference has also come to the foreground as a mean to get even greater insights by our observational data but that is a step following standard regression analysis techniques.
Finally, it is notable that while permutation tests are indeed a type of hypothesis testing, A/B testing is a type of an experimentation approach to maximise business goals and using statistical hypothesis testing is only part of the methodology required (parametric or non-parametric, frequentist or Bayesian, etc). For example, multi-armed bandits is another experimentation approach to solve the same problem. Ultimately what we want to maximise our utility functions (e.g. time on client, user expenditure, etc.) That is not what hypothesis testing is aiming to do as it is much more interested in the truthfulness of a hypothesis.
