Merge pull request #279552 from wmwxwa/patch-22

Links to ai-agents.md

Author: prmerger-automator[bot]

articles/cosmos-db/introduction.md

@@ -22,7 +22,7 @@ Today's applications are required to be highly responsive and always online. The
 
 The surge of AI-powered applications created another layer of complexity, because many of these applications integrate a multitude of data stores. For example, some organizations built applications that simultaneously connect to MongoDB, Postgres, Redis, and Gremlin. These databases differ in implementation workflow and operational performances, posing extra complexity for scaling applications.
 
-Azure Cosmos DB simplifies and expedites your application development by being the single database for your operational data needs, from [geo-replicated distributed caching](https://medium.com/@marcodesanctis2/using-azure-cosmos-db-as-your-persistent-geo-replicated-distributed-cache-b381ad80f8a0) to backup to [vector indexing and search](vector-database.md). It provides the data infrastructure for modern applications like AI, digital commerce, Internet of Things, and booking management. It can accommodate all your operational data models, including relational, document, vector, key-value, graph, and table.
+Azure Cosmos DB simplifies and expedites your application development by being the single database for your operational data needs, from [geo-replicated distributed caching](https://medium.com/@marcodesanctis2/using-azure-cosmos-db-as-your-persistent-geo-replicated-distributed-cache-b381ad80f8a0) to backup to [vector indexing and search](vector-database.md). It provides the data infrastructure for modern applications like [AI agents](ai-agents.md), digital commerce, Internet of Things, and booking management. It can accommodate all your operational data models, including relational, document, vector, key-value, graph, and table.
 
 ## An AI database providing industry-leading capabilities...
 

articles/cosmos-db/vector-database.md

@@ -35,7 +35,7 @@ There are two common types of vector database implementations - pure vector data
 
 A pure vector database is designed to efficiently store and manage vector embeddings, along with a small amount of metadata; it is separate from the data source from which the embeddings are derived.
 
-A vector database that is integrated in a highly performant NoSQL or relational database provides additional capabilities. The integrated vector database in a NoSQL or relational database can store, index, and query embeddings alongside the corresponding original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, keeping the vector embeddings and original data together better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance.
+A vector database that is integrated in a highly performant NoSQL or relational database provides additional capabilities. The integrated vector database in a NoSQL or relational database can store, index, and query embeddings alongside the corresponding original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, keeping the vector embeddings and original data together better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance. A highly performant database with schema flexibility and integrated vector database is especially optimal for [AI agents](ai-agents.md).
 
 ### Vector database use cases