This template presents a multi-agent system in which a coordinating agent manages specialized sub-agents: an AI agent for RAG and document summarization, and an email agent. Each agent effectively operates in its own domain, working collaboratively under the management of the primary agent.
In addition to the two sub-agents, the coordinator agent queries the latest news by calling the HTTPS Request Tool.
💡 This template is an extended version of the initial workflow on how to Build a RAG Agent with n8n, Qdrant & OpenAI.
The RAG sub-agent can use the same Qdrant collection. You can import this example collection (n8n-rag-2437367325990310-2025-11-04-10-41-54.snapshot) of 3 documents into the free Qdrant cloud or self-hosted account, rather than creating it from scratch.
The template uses the following example files in the Google Docs format:
1️⃣ Ask the agent about specific information, facts, quotes, or details that are stored in the uploaded documents.
E.g. What should be documented during incident response?
2️⃣ Ask the agent about recent news and current information from web sources.
E.g. What does BDSG say about data breaches and are there any recent cases?
3️⃣ Ask the agent to summarize the document or information related to the documents and email it to you.
E.g.I need a short summary of the Berkshire Hathaway letter, please send it to my email [[email protected]].
4️⃣ Aks the agent to update you on your recent emails.
E.g. I’d like to know the content of the latest email from [username].
5️⃣ Ask the agent to create a draft of the email.
E.g. Please create an email draft of the [document] summary.
⚠️ The current multi-agent architecture comes with certain trade-offs: the sequential nature of agent hand-offs can increase latency compared to single calls, and the full conversation history is not shared between all sub-agents.
💻 📞Get in touch if you want to customise this workflow or have any questions.