AgentCore Dashboard
HubSpot
Marketing & Sales Data
Salesforce
Enterprise CRM
Notion
Corporate Knowledge Base
Snowflake
Data Warehouse
Databricks
Lakehouse Analytics
PostgreSQL
Vector Storage DB
Legacy Database
On-Premises Data
AgentCore OS
Modern Web Apps
Next.js / React (VTAWEB)
Enterprise Dashboards
BI Reports & Visualizations
Omnichannel Bots
Slack / Teams Integrations
HubSpot
Marketing & Sales Data
Salesforce
Enterprise CRM
Notion
Corporate Knowledge Base
Snowflake
Data Warehouse
Databricks
Lakehouse Analytics
PostgreSQL
Vector Storage DB
Legacy Database
On-Premises Data
AgentCore - The complete AI Agent Tech Stack
Modern Web Apps
Next.js / React (VTAWEB)
Enterprise Dashboards
BI Reports & Visualizations
Omnichannel Bots
Slack / Teams Integrations
USER
Provides input or request
AI AGENT
Plans, reasons, and takes action
TOOLS
Uses tools and external systems
MEMORY
Reads/Writes context
RESPONSE
Delivers output to the user
GOAL
Autonomously solve problems & deliver value.
Foundation
Model
Orchestration
Framework
Memory
Systems
Vector Database
and RAG
Tools and
Integrations
Observability
and Evaluation
Deployment
Infrastructure
Documents
Embeddings
Model
Vector
Database
and store in vector database
Query
Embeddings
Model
Vector
Search
Retrieved
Chunks
LLM
Answer
retrieve relevant chunks, and generate answer with LLM
Why Retrieval-Augmented Generation?
Foundation models know a lot, but they do not know your documents. They were not trained on your internal knowledge base, your customer support history, your proprietary research, or anything that has happened since their training cutoff. Retrieval-Augmented Generation (RAG) is how you fix that.
The concept is straightforward: instead of trying to fit an entire knowledge base into the context window, you convert your documents into numerical representations (embeddings), store them in a vector database, and retrieve only the most relevant chunks at query time. The agent gets a context window full of precisely the right information rather than everything you have ever written.
📈 The global vector database market reached $3.2 billion in 2025 and is growing at 24% annually, which reflects how central retrieval has become to production AI systems.
The Vector Database Landscape
The leading options each serve a different use case:
Pinecone
Fully managed with zero infrastructure overhead. You pay for it, push vectors to it, and query it. At 100M+ vectors, it maintains recall without tuning. The right choice when you want to ship and not think about infrastructure.
Weaviate
Open-source with a managed cloud option. Leads the field on hybrid search combining vector similarity, keyword matching (BM25), and metadata filtering. If your retrieval needs more than pure semantic search, Weaviate handles it natively.
Chroma
Developer-first and runs locally with no infrastructure. The 2025 Rust rewrite made it significantly faster. It is the right choice for prototyping and small-to-medium production workloads where developer experience matters more than scale.
pgvector
A PostgreSQL extension that adds vector search to a database you may already be running. The lowest-friction path to production RAG. It handles millions of vectors with HNSW indexing and stays within single-node limits for most workloads.