Virtual helpers like Siri, Alexa, and Google Assistant are now a big part of everyday life. They do things by listening to your words and responding to normal language. Even though these systems are getting better at recognizing speech and making responses, one important area that needs more research is how they can understand and make meaningful speech acts. This paper uses speech act theory to look at how well these helpers can recognize and carry out different kinds of illocutionary acts, such as spokespeople, orders, and commissives. The study looks at Austin's and Searle's basic theories to compare how each helper handles and reacts to different practical tasks. This study uses planned trials with 60 speech act-based questions from real-life usage situations to test the helpers' ability to keep pragmatic consistency, figure out the goal, and give the right context. They show that the processes are not at all the same. When it comes to giving information and directions, Google Assistant is the best. When it comes to action-based orders, Alexa is the best. Siri is good at giving answers that show how you feel, but it's not great at acts that aren't clear or straight. They show how important pragmatic analysis is for judging AI conversation and give us thoughts on how to improve natural language tools. By combining language theory with real-world AI testing, this study adds to the fields of human-computer interaction, AI pragmatics, and discourse technology.