We’ll explore how they transitioned from early feature experiments to a robust GenAI platform, and eventually to multi-agent systems capable of reasoning, planning, and collaborating at scale.

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LinkedIn evolved its GenAI infrastructure from fragmented experiments to a unified platform supporting multi-agent systems. The company shifted from Java to Python for both offline and online development, adopted LangChain as its primary framework, and built centralized systems for prompt management, skill registries, and memory. The platform leverages existing messaging infrastructure for agent orchestration, implements strict privacy controls, and uses OpenTelemetry for production observability. Key architectural decisions include keeping abstractions thin for flexibility, using human-in-the-loop controls for critical actions, and building reusable components that enable teams to ship AI features faster while maintaining consistency and trust.

The Evolution of LinkedIn’s Generative AI Tech Stack

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