LinkedIn builds Cognitive Memory Agent to enable personalized, stateful AI agents at scale
LinkedIn's AI agents needed persistent memory to learn from interactions and deliver personalized experiences, but traditional memory systems required users to explicitly specify what to remember, and an earlier in-house experiential memory store provided infrastructure with no intelligence, forcing every application team to build custom extraction and retrieval logic.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User activity data ingested
Raw user activity and conversation data are indexed into various memory formats via streaming and batch pipelines before being stored.
Tools used
Cognitive Memory AgentHiring AssistantCouchbaseEspressoopen-source LLMvector store
Outcome
LinkedIn built the Cognitive Memory Agent (CMA), a horizontal memory platform powering stateful, context-aware AI agents at scale, now applied in the globally available Hiring Assistant to personalize recruiter interactions and reduce friction by automatically surfacing past preferences.
What failed first
LinkedIn's initial experiential memory was a hierarchical key-value store built atop Couchbase and Espresso with no intelligence; each application team had to manually build preference extraction, indexing, and retrieval logic, making every integration custom-built and preventing ubiquitous adoption.