This topic examines how vector databases and retrieval-augmented generation (RAG) platforms form the retrieval backbone for enterprise agents that need memory, context and up-to-date knowledge. Vector stores (Pinecone, Weaviate, Milvus, RedisVector) enable fast semantic search over embeddings, while RAG layers orchestrate retrieval, filtering, and prompt assembly before generation. Agent frameworks and marketplaces—represented by tools like LangChain, IBM watsonx Assistant, Yellow.ai and PolyAI—connect those retrieval components to orchestration, evaluation and delivery across channels. Relevance is driven by enterprise demand for reliable, low-latency knowledge access, data governance, and multi-turn agent behavior. Organizations are prioritizing hybrid search (semantic + keyword), multimodal embeddings, index scalability, and observability to meet SLAs and compliance. LangChain provides the engineering scaffolding to build, test and deploy agentic LLM applications and integrate vector stores; IBM watsonx Assistant focuses on enterprise-grade virtual agents and no-code/developer workflows; Yellow.ai targets CX/EX automation with multi-channel agent deployments; PolyAI emphasizes voice-first contact center agents. Each plays a different role in the agent stack: vector DBs store and serve embeddings, RAG platforms and middleware manage retrieval and context, and agent platforms handle orchestration, dialog policy and channel integration. For enterprise selection, important considerations include query latency, index update patterns, vector index types, memory and storage costs, security/GDPR controls, and tooling for evaluation and retraining. Understanding how vector databases and RAG integrate with agent frameworks clarifies trade-offs between control, speed and compliance when deploying production-grade AI agents across search, contact center and automation use cases.