How LinkedIn Built Their First AI Agent for Hiring with LangGraph

Agent Adoption & Scalability - LinkedIn aims to scale agentic adoption within the organization to enable broader idea generation [2] - LinkedIn built the Hiring Assistant, its first production agent, to automate recruiter tasks and free up time for candidate interaction [3] - The Hiring Assistant follows an ambient agent pattern, operating in the background and notifying recruiters upon completion [4][5] - LinkedIn adopted a supervisor multi-agent architecture, with a supervisor agent coordinating sub-agents that interact with LinkedIn services [6] Technology Stack & Framework - LinkedIn standardized on Python for GenAI development, moving away from its traditional Java-centric approach [7][8] - The company built a service framework using Python, gRPC, Langchain, and Langraph to streamline the creation of production-ready Python services [9][19] - Over 20 teams have used this framework to create over 30 services supporting Generative AI product experiences [9][10] - Langchain and Langraph were chosen for their ease of use and sensible interfaces, enabling rapid development and integration with internal infrastructure [22][23] Infrastructure & Architecture - LinkedIn invested in a distributed architecture to support agentic communication modes [10] - The company modeled long-running asynchronous flows as a messaging problem, leveraging its existing messaging service for agent-to-agent and user-to-agent communication [26][27] - LinkedIn developed agentic memory with scoped and layered memory types (working, long-term, collective) [29][30] - LinkedIn implemented a centralized skill registry, allowing agents to discover and access skills developed by different teams [34][35]