JARVIS acts as an intelligent intermediary between users and a network of specialized agents. When a user interacts with the system, their message is directed to JARVIS as the primary point of contact. This initial step is where the magic begins to unfold. After understanding the user need. JARVIS navigates through a repository of specialized agents, each programmed to excel in specific tasks. Whether it's fetching information, performing calculations, or executing complex actions, JARVIS knows just the right agent for the job. Upon identifying the ideal agent, JARVIS initiates a seamless handover. The chosen agent becomes active, taking on the responsibility of fulfilling the user's request. This activation process extends to both the frontend and backend components, ensuring a cohesive and synchronized interaction between the user, JARVIS, and the chosen agent. Rather than users needing to interact with multiple agents individually, JARVIS simplifies the experience by acting as a gatekeeper. Users interact with a single point of contact, making their queries and requests in natural language, while JARVIS handles the intricate orchestration behind the scenes. To exhibit our system's potential, we've crafted a user-friendly web interface, sidestepping authentication complexities. Inside, two prototype agents—"music" and "call"—showcase our concept's prowess. As we look towards the future, our vision encompasses the integration of an expanding repertoire of specialized agents. This entails leveraging the power of prompt engineering to craft prompts that elicit precise and effective responses from the agents. By refining these prompts and training the agents, we aim to elevate the system's accuracy and versatility, enabling it to address an ever-widening array of user needs and inquiries.Category tags:
"JARVIS is all-in-one your personal assistant. Wonderful, the app works so smoothly. The presentation is well crafted although the video presentation exceeded the time limit and the ppt completely missed out on the revenue model and the other business propositions of the application. But other than that, wonderful work, and all the best for future endeavors. "
Machine Learning Engineer