Connect Notion & Confluence to Your AI Agents

Your best answers already live in Notion and Confluence — but they sit in pages nobody re-reads and a search box that only finds exact words. Connecting those wikis to your AI agents turns static documentation into a live, queryable knowledge base your agents can ground their answers in.

Why connect Notion to AI instead of copy-pasting

Most teams start by pasting a few paragraphs into a chat prompt. That works once, then breaks: the content goes stale the moment someone edits the source page, and you have no idea which sentence the model actually used. A proper integration solves both problems. When you connect Notion to AI through AgentWorks, your pages become part of a retrieval layer that stays in sync with the source and returns citations pointing back to the exact page a claim came from.

The difference matters most for the questions people actually ask: "What's our refund policy?", "How do we onboard a new customer?", "Which environment do we deploy to on Fridays?" These answers exist in your wiki. The job is to make them retrievable in natural language, with a clear trail back to the source — not to have a model guess. AgentWorks' knowledge & RAG layer is built for exactly this.

How AgentWorks turns wikis into a knowledge base

Under the hood, AgentWorks ingests your connected sources and stores them in a vector index (pgvector) so agents can retrieve the most relevant passages for any question — this is retrieval-augmented generation, or RAG. You point the platform at your content and it handles chunking, embedding and retrieval.

You can combine several source types in one knowledge base:

• Notion & Confluence — connect your existing wikis directly, alongside Jira, Asana, Monday and other tools.
• Uploaded documents — PDF, DOCX, TXT and CSV files.
• URLs — public pages you want the agent to reference.

Because everything lands in the same index, an agent answering a question can pull from a Confluence runbook, a Notion product spec and an uploaded PDF in a single response — and cite each one.

Grounded answers, with citations and honest gaps

The point of grounding is trust. When an AgentWorks agent answers from your knowledge base, it returns citations so you can click through to the underlying Notion or Confluence page and verify the claim yourself. Just as important: when the answer genuinely isn't in your knowledge base, the agent says "I don't know" rather than inventing something plausible. That single behaviour is what makes a knowledge base safe to put in front of employees or customers.

This grounding works across models. In multi-LLM chat you can switch between GPT-5, Claude, Gemini and Mistral Large mid-conversation, and your connected knowledge travels with you — the same cited passages are available whichever model you're using for a given question. Gemini's up-to-1M-token context is useful when you're pulling in large stretches of documentation at once.

Privacy and governance for your wiki content

Wiki content is sensitive — it often contains customer names, internal processes and commercial detail. AgentWorks is EU-native (built in the Netherlands) and treats that content accordingly. Personally identifiable information is masked at the gateway before any prompt reaches a model, so raw PII from your pages doesn't leave the boundary unprotected. Where EU model endpoints are offered, your data stays on EU infrastructure, and AgentWorks works under no-training, zero-retention model contracts so your Notion and Confluence content isn't used to train third-party models.

Every agent run is captured in an immutable, append-only audit trail you can export as CSV or JSON, and state-changing actions can require human-in-the-loop approval. If you operate under the EU AI Act, per-agent risk classification helps you map each use case to the right controls — the platform is EU AI Act-ready, meaning it gives you the mechanisms, while actual risk depends on how you use it. You can read more on the compliance and trust pages.

From a single agent to an automated workflow

A connected knowledge base is more useful the more you build on it. On the Free plan you get 50+ pre-built AI agents and up to three integrations, which is enough to wire Notion or Confluence into a support or research assistant and see grounded answers immediately. On Pro and above you can chain steps into multi-agent pipelines — for example research → draft → review → publish — where one agent retrieves from your wiki, another drafts a reply, and a third checks it before anything ships.

Those pipelines can run on a schedule (daily, weekly or monthly) or fire from a webhook, so a "summarise this week's changed Confluence runbooks" job can land in Slack every Monday without anyone lifting a finger. Every step is logged and carries its own risk class. See pricing for what each plan includes.

Summary: Connecting Notion and Confluence to AgentWorks turns your existing wikis into a live, cited knowledge base. Agents retrieve the right passages via RAG, answer with citations, say "I don't know" when content is missing, and run under EU-native privacy controls — PII masking, EU data residency where offered, and an exportable audit trail.

Frequently asked questions

How do I connect Notion to AI agents in AgentWorks?

Add Notion (and Confluence) as a knowledge source from your workspace, and AgentWorks ingests the pages into its pgvector-backed index. The Free plan supports up to three integrations, so you can connect a wiki and start getting grounded, cited answers without upgrading.

Will my Notion and Confluence content be used to train AI models?

No. AgentWorks operates under no-training, zero-retention model contracts, and PII is masked at the gateway before any content reaches a model. Where EU model endpoints are offered, your data stays on EU infrastructure.

What happens if the answer isn't in my wiki?

The agent tells you it doesn't know rather than fabricating a response. Grounded retrieval means answers come from your connected sources with citations, so you can always trace a claim back to the specific Notion or Confluence page it came from.