"LinkedIn has extended its generative AI application platform to support multi-agent systems by repurposing its existing messaging infrastructure as an orchestration layer. This approach allows the company to scale AI agent deployments without building new coordination technology from scratch. The revised architecture enables LinkedIn's "Hiring Assistant", the company's first AI agent, to achieve global availability while supporting complex multi-step workflows through agent coordination."
"Reusing existing infrastructure and providing strong developer abstractions are key to scaling complex AI systems efficiently. Designing for human-in-the-loop control ensures trust and safety while enabling agents to operate autonomously when appropriate. Observability and context engineering have become essential for debugging, continuous improvement, and delivering adaptive, personalized experiences. Finally, adopting open protocols is critical to enabling interoperability and avoiding fragmentation as agent ecosystems grow."
"The platform treats agents as standardized gRPC services registered in a central skill registry, allowing developers to define agent capabilities through familiar service contracts. "Developers simply annotate this definition with some platform-defined proto3 options that describe the metadata of their agent, and register it via a build plugin into the skill registry", creating a reusable agent-as-a-service pattern that leverages existing LinkedIn infrastructure."
LinkedIn repurposed its existing messaging infrastructure as an orchestration layer to support multi-agent generative AI and scale agent deployments without building new coordination technology. The revised architecture enabled the Hiring Assistant to achieve global availability and handle complex multi-step workflows through agent coordination. Agents are implemented as standardized gRPC services registered in a central skill registry, with developers annotating proto3 metadata and registering via a build plugin to create an agent-as-a-service pattern. The messaging platform provides FIFO delivery, message persistence, and horizontal scaling. Key architectural lessons include reusing infrastructure, strong developer abstractions, human-in-the-loop control, observability, context engineering, and open protocols.
#multi-agent-systems #messaging-orchestration #grpc-skill-registry #human-in-the-loop #observability
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