20 · Enterprise RAG + Agent System

This is a production-ready enterprise RAG + agent system architecture with LangChain and Bedrock AgentCore — an enterprise assistant that can reason over your data, use tools and follow policies.

How it works

1. 1Data sources (S3, Confluence, SharePoint, databases, CRM) feed an ingestion pipeline that chunks, embeds and indexes content.
2. 2The AgentCore runtime orchestrates the agent with memory, tools (MCP) and identity.
3. 3The knowledge layer (RAG) retrieves grounded context; the model layer is Bedrock.
4. 4Responses stream back with structured output and citations.
5. 5Security, evaluation and observability wrap the whole flow.

Key points

• Combines retrieval (RAG) with action-taking agents in one architecture.
• Built-in telemetry via CloudWatch/OpenTelemetry + LangSmith.
• Security and governance: IAM, Cedar policies, guardrails, audit logs.
• Modular, observable and built for enterprise scale.

Why it matters

Real enterprise assistants need both grounded knowledge (RAG) and the ability to act (agents/tools). This architecture unites them securely and observably.

Frequently asked questions

How is this different from plain RAG?

It adds agents and tools, so the system can act on data, not just answer questions about it.

What keeps it secure?

IAM, Cedar policies, guardrails and audit logs across the runtime and tools.