How to build a powerful Confluence AI agent in 2025

If your company uses Confluence, it’s probably stuffed with useful information. It’s the home for everything from product specs and marketing strategies to those all-important HR policies. But let’s be honest, finding what you need in that mountain of documents can be a real headache. And using that knowledge to power any kind of automated support? Even tougher.

This is where a Confluence AI agent comes in. The idea is to turn all that documentation into a smart resource that can answer questions on the spot and handle simple tasks. This guide will walk you through the options you have, from Atlassian’s own tools to more flexible third-party platforms, so you can figure out the best way to put your company’s collective brain to work.

What is a Confluence AI agent?

Simply put, a Confluence AI agent is an AI tool that uses the information stored in your Confluence spaces as its source of truth. Instead of your team digging through pages to find an answer, they (or your customers) can just ask a question in plain English, and the AI will find and deliver the right information.

You generally have two ways to set this up:

1. Go native: Use the AI features that Atlassian has built directly into its products, like Atlassian Intelligence and Rovo. These tools are baked right in, but they mostly stick to the Atlassian world.

2. Use a third-party platform: Connect Confluence to a specialized AI platform that can also plug into your other tools (think helpdesks, chat apps, and other document storage). This approach brings all your company knowledge together, not just the bits living in Confluence.

Let’s look at both, starting with the out-of-the-box option from Atlassian.

The native Confluence AI agent: Atlassian Intelligence and Rovo

Atlassian has been building out its own AI capabilities under the "Atlassian Intelligence" brand, with Rovo as its new AI "teammate." It’s designed to work smoothly across Confluence, Jira, and Jira Service Management.

Key features of Atlassian’s AI

Rovo is meant to feel like a natural extension of your Confluence workflow. It uses your existing data to help with a few key tasks:

• Content creation: You can give it a simple prompt to draft a new page, blog post, or project outline.

• Summarization: It can give you the highlights of a long document, a comment thread, or a page’s edit history so you don’t have to read through everything.

• AI-powered search: You can ask it questions conversationally and get answers pulled together from relevant Confluence pages.

• Automation: You can set up rules to trigger actions, like notifying a team in chat when a new product spec is published.

Atlassian’s AI pricing

Here’s the catch: Atlassian Intelligence features, including Rovo, aren’t on every plan. They’re typically included in the more expensive cloud subscriptions. The pricing can also be a bit hard to predict since it’s based on things like AI credits and indexed objects.

Plan	Price (per user/month, annual)	Key AI Features & Limits
Free	$0	No Atlassian Intelligence features.
Standard	$5.16	Rovo Search, Chat & Agents with 25 AI credits/user/month and 100 indexed objects/user.
Premium	$9.73	Everything in Standard, plus 70 AI credits/user/month and 250 indexed objects/user.
Enterprise	Custom (Billed Annually)	Everything in Premium, plus 150 AI credits/user/month and 625 indexed objects/user.

Source: Pricing and feature details are based on Atlassian’s Confluence pricing page.

The limitations of a native-only approach

While using Atlassian’s built-in AI is convenient, it can be pretty limiting if your team’s work isn’t happening 100% inside the Atlassian ecosystem.

The biggest issue is knowledge silos. Rovo’s brain is mostly fed by your Atlassian products and a handful of other apps. This means important context from customer conversations in Zendesk, solutions shared in Slack, or project docs stored in Google Drive gets left out. Your AI agent ends up with only part of the story, which often leads to incomplete or unhelpful answers.

Getting access to these AI features also means upgrading to a Premium or Enterprise plan, which is a big jump in cost. The credit-based system adds another layer of complexity, making it tough to budget your expenses from month to month.

Finally, native tools often don’t have the flexibility needed for specific support situations. You get very little say over the AI’s personality, how it escalates tricky questions, or what custom actions it can perform, like looking up a customer’s order status from a Shopify store.

For teams that need something more connected, flexible, and predictable, a third-party platform usually makes more sense.

The third-party Confluence AI agent: Unifying all your knowledge

A third-party Confluence AI agent gets around the main problem of the native approach: siloed information. Instead of just being an AI for Confluence, it allows Confluence to become one piece of a much larger knowledge base that spans your whole company.

This is the approach taken by platforms like eesel AI. It’s built to connect to all your different knowledge sources and put an AI to work wherever you need it, whether that’s inside your helpdesk, your team’s chat, or on your public website.

A screenshot of the eesel AI platform connecting to multiple business applications to build a comprehensive knowledge base for the Confluence AI agent.

Why a unified approach is more effective

When you connect Confluence to a central hub like eesel AI, you get a much smarter and more capable system.

The main difference is that it can connect to everything. You can link Confluence right alongside your helpdesk (like Zendesk or Freshdesk), other wikis (Google Docs or Notion), and your internal chat tools (Slack or Microsoft Teams). The AI learns from all of it, giving it the full picture it needs to answer questions correctly.

It can also learn from your team’s past conversations by training on historical helpdesk tickets. This means it doesn’t just know what your official docs say; it understands the real problems your customers have, your brand’s tone of voice, and the solutions that have actually worked before.

Getting started is also surprisingly straightforward. You can connect your apps and launch an AI agent in minutes on your own, without having to sit through a sales demo. It just plugs into the tools you already use.

This image shows the variety of applications a Confluence AI agent like eesel AI can integrate with, creating a unified knowledge base.

And before you set it live, you can test it out. eesel AI has a simulation mode that lets you run the agent on thousands of your past tickets. You can see exactly how it would have replied, which gives you a good idea of how it will perform and lets you tweak its responses using a simple prompt editor. No surprises.

A look at the testing and simulation feature, where you can see how the Confluence AI agent would have responded to past inquiries, ensuring its accuracy before going live.

Practical use cases for a unified AI agent

When your Confluence AI agent can draw from a unified knowledge base, it can handle situations that the native tools just can’t. Here are a couple of practical use cases of what that looks like.

Powering instant internal support in Slack

An employee has a question about the company’s expense policy. Instead of bugging someone in a public channel or trying to find the right page in Confluence, they just ask the eesel AI bot in a direct message. The bot, which has learned from the official HR docs in Confluence, gives an immediate, correct answer and even provides a link to the source page. Fewer interruptions for the HR team, and employees get what they need right away. This is a great example of internal support in Slack.

A screenshot showing the eesel AI Confluence AI agent answering an employee's question directly within Slack, providing instant internal support.

Automating frontline customer service in Zendesk

A customer sends in a support ticket asking how to set up a new feature. The eesel AI agent reads the request, pulls the setup guide from your Confluence knowledge base, and checks it against similar past tickets from Zendesk to give a clear, step-by-step answer. Automating frontline customer service like this resolves tickets in seconds, so your support team can focus on more complex problems.

This screenshot demonstrates the Confluence AI agent acting as a copilot within a helpdesk, drafting a detailed response to a customer query.

Proactively improving your documentation

The AI’s analytics dashboard might show you that customers are constantly asking about a topic that isn’t covered well in your Confluence docs. The system can then use the successful answers from your helpdesk tickets to generate a draft article for you. Your documentation team can give it a quick review, polish it up, and publish it, closing that knowledge gap for good.

The analytics dashboard of the Confluence AI agent, highlighting knowledge gaps and deflection rates to proactively improve documentation.

This video provides a step-by-step guide on how you can start building your own custom AI agents with Atlassian Rovo in just a few minutes.

Choose a Confluence AI agent that breaks down silos

While Atlassian’s native AI is a decent place to start, its knowledge is mostly stuck inside the Atlassian world. That means you get an AI that only sees a small part of your business.

To build a Confluence AI agent that’s truly helpful, you need a platform that can bring all your knowledge together, learn from how your team actually works, and give you the control to automate with confidence. By connecting Confluence to a central AI brain like eesel AI, you turn your documentation from a quiet library into an active, intelligent resource that helps out your entire organization.

Ready to see what your Confluence knowledge is really capable of?

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