This paper proposes a system built on AI technology that uses multiple agents to completely take over the process of creating structured support tickets from user issues coming in via email, WhatsApp, or voice messages. The system is composed of various agents that cooperate together and this modular architecture makes efficient ticket generation the main feature of the system. First of all, a text preprocessing agent takes care of cleaning and normalizing the data before it is processed further. Then, the issue categorization agent assigns the issues to the corresponding predefined taxonomies. A summarization agent is also involved in the process; it takes the long inputs and condenses them into shorter ones. Also, a detail extraction agent is in the loop as it uses large language models (LLMs) along with state-of-the-art NLP methods to locate crucial pieces of information, for instance, the type of the issue, the product affected, and short descriptions. Then, a ticket creation agent takes care of formatting and filling in the standardized templates while also a voice transcription agent is at work converting the audio messages into text. This system is using LLMs, named entity recognition, and sentiment analysis as its main tools for the purpose of determining issue criticality and assigning priorities according to SLA rules. A module aimed at the detection of duplicates is constantly running in the background and its main goal is to minimize redundancy by checking the new incoming issues with the already existing tickets. Besides that, there is a translation unit that works with several languages allowing problems to be raised and solved in any part of the world. The helpdesk tools among them Jira, are giving support to the system by auto updating the tickets and synchronizing the workflow with real-time Slack notifications that increase the coordination of the team. The robustness of the system architecture makes it possible for the system to scale easily and be changed to fit new channels and features. The system will be evaluated with respect to the real-world datasets used for ticket accuracy, priority assignment, duplicate detection, processing time, and user satisfaction. The ultimate aim is to create a more effective and faster support system in multi-channel environments.