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AI agents are one of the hottest topics in tech right now — but how many enterprises have actually deployed and are actively using them?
LinkedIn says it has with its LinkedIn hiring assistant. 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.
“This is not a demo product,” Deepak Agarwal, chief AI officer at LinkedIn, said onstage this week at VB Transform. “This is live. It’s saving a lot of time for recruiters so that they can spend their time doing what they really love to do, which is nurturing candidates and hiring the best talent for the job.”
Relying on a multi-agent system
LinkedIn is taking a multi-agent approach, using what Agarwal described as 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 happens through the supervisor agent, which takes input from human users around 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.
“Then you can collaborate with it, right?” said Agarwal. “You can modify it. No longer do you have to talk to the platform in keywords. You can talk to the platform in natural language, and it’s going to answer you back, it’s going to have a conversation with you.”
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 Agarwal.
He emphasized the importance of “human first” agents that keeps users always in control. 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.
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