Going beyond its popular recommender systems and AI-powered search, the company’s AI agent sources and recruits job candidates through a simple natural language interface. LinkedIn is taking a multi-agent approach, using a collection of agents collaborating to get the job done. A supervisor agent orchestrates all the tasks among other agents, including intake and sourcing agents that are “good at one and only one job.” All communication occurs through the supervisor agent, which receives input from human users regarding role qualifications and other details. That agent then provides context to a sourcing agent, which culls through recruiter search stacks and sources candidates along with descriptions on why they might be a good fit for the job. That information is then returned to the supervisor agent, which begins actively interacting with the human user. The agent can then refine qualifications and begin sourcing candidates, working for the hiring manager “both synchronously and asynchronously.” “It knows when to delegate the task to what agent, how to collect feedback and display to the user,” said Deepak Agarwal, chief AI officer at LinkedIn. The goal is to “deeply personalize” experiences with AI that adapts to preferences, learns from behaviors and continues to evolve and improve the more that users interact with it. LinkedIn provides engineers with different algorithms based on RL, supervised fine tuning, pruning, quantization and distillation to use out of the box without worrying about GPU optimization or FLOPS, so they can begin running algorithms and training, said Tejas Dharamsi, LinkedIn senior staff software engineer. In building out its models, LinkedIn focuses on several factors, including reliability, trust, privacy, personalization and price, he said. Models must provide consistent outputs without getting derailed. Users also want to know that they can rely on agents to be consistent; that their work is secure; that past interactions are being used to personalize; and that costs don’t skyrocket.