This explains the tendency of agent-based applications to fall back on messaging architectures. Ramgopal points out, “The reason we and almost everyone else are falling back to messaging as the abstraction is because it’s incredibly powerful. You have the ability to communicate in natural language, which is, you know, pretty important. You have the ability to attach structured content.” The use of structured and semistructured information is becoming increasingly important for agents and for protocols like A2A, where much of the data is from line-of-business systems or, in the case of LinkedIn’s recruitment platform, stored in user profiles or easy-to-parse resumes.
The orchestrating service can assemble documents as needed from the contents of messages. At the same time, those messages give the application platform a conversation history that delivers a contextual memory that can help inform agents of user intent, for example, understanding that a request for available software engineers in San Francisco is similar to a following request that asks “now in London.”
Building an agent life-cycle service
At the heart of LinkedIn’s agentic AI platform is an “agent life-cycle service.” This is a stateless service that coordinates agents, data sources, and applications. With state and context held outside this service in conversational and experiential memory stores, LinkedIn can quickly horizontally scale its platform, managing compute and storage like any other cloud-native distributed application. The agent life-cycle service also controls interactions with the messaging service, managing traffic and ensuring that messages aren’t dropped.
