A guide to Rovo Deep Research reasoning and its alternatives

Let’s be honest, we’ve all had that moment. You know the answer you need is floating around somewhere in the company's digital universe, but is it in a Slack thread from last Tuesday? A Confluence page from six months ago? Or that one Google Doc nobody's touched in a year? Trying to piece it all together can feel like you need a detective's license just to do your job.

Atlassian is throwing its hat in the ring with Rovo, its AI assistant for enterprise teams. One of its most talked-about skills is "Deep Research," which acts like a personal research analyst, digging through all that data to build detailed reports for you.

But how does it actually connect the dots? In this guide, we'll pull back the curtain on how Rovo Deep Research Reasoning works, figure out what it’s really good at, and look at where it might not be the right fit, particularly for teams that need to get things done, not just read about them.

What is Atlassian Rovo Deep Research?

Rovo Deep Research is a special skill baked into Rovo Chat, Atlassian's AI assistant that pops up across their products. Its main purpose is to handle big, open-ended questions like, "What was all the customer feedback for Project Phoenix last quarter?" and then deliver a polished, structured report.

It's built to feel right at home inside the Atlassian world, using what the company calls the "Teamwork Graph." You can think of this graph as Rovo's internal cheat sheet for your company. It helps the AI understand the context of your projects, your teams, and all the internal lingo you use in tools like Jira and Confluence. It’s the map that shows how all the pieces of your work connect.

This isn’t your average search function. It’s a whole process that involves planning, grabbing information from a bunch of different places at the same time, and then stitching it all together into something you can actually use.

How Rovo Deep Research Reasoning works

The secret sauce behind Rovo Deep Research is the step-by-step way it tackles your questions, a process Atlassian is refreshingly open about. If you want to get a feel for the tool's strengths and weaknesses, you need to understand this process. This is the heart of Rovo Deep Research Reasoning.

Rovo's multi-step reasoning process

When you ask Rovo a complicated question, it doesn't just start Googling your internal docs. It creates a little game plan to make sure the answer is as good as it can be.

1. Step 1: It breaks your question down into a plan. Before doing anything else, Rovo dissects your big request into smaller, bite-sized tasks. For instance, if you ask it to analyze customer feedback, it might create a to-do list like: "find all feedback tickets," "sort feedback into themes," and "pinpoint the top three feature requests." This becomes its roadmap for the whole operation.

2. Step 2: It searches everywhere at once. Instead of working through its to-do list one item at a time, Rovo goes after everything in parallel. It dives into all of your connected data sources simultaneously, which helps it pull together a ton of information much faster than a one-track-mind approach.

3. Step 3: It stops, thinks, and tries again. After its initial search, Rovo takes a breather to review what it’s found. It gets rid of anything that isn't relevant and looks for any obvious holes in the information. If it needs more detail on a specific point, it will run another, more focused search. This back-and-forth is a key part of its "reasoning" and lets it get progressively deeper until it feels confident in its findings.

4. Step 4: A specialized agent writes the final report. Once all the information is gathered, a different AI agent takes over with one job: synthesis. It organizes the findings into a clear outline, writes each section, and adds citations that link right back to the original documents. This is a pretty neat feature, as it lets you easily check where the AI got its information.

Rovo Deep Research: Key features and use cases

Rovo’s methodical way of thinking makes it a great fit for certain internal, analytical tasks where you need to connect dots from all over the company.

Core capabilities

• It gets your inside jokes. Thanks to that "Teamwork Graph," Rovo understands the unique language of your company. It knows that "Project Chimera" is the internal code name for your next big launch, not a mythical beast, which keeps its results relevant.

• It shows its work. Trust is a big deal with AI. Rovo helps by citing its sources for every major point in its reports. You can click a link and land on the original Jira ticket or Confluence page, giving you peace of mind that the AI isn't just pulling facts out of thin air.

• It uses a team of AI models. Atlassian doesn't rely on a single large language model. It uses a mix of models from different providers like OpenAI and Anthropic, assigning each one to the task it’s best at. One model might be great at planning, another at reasoning, and a third at writing. Using the best tool for each part of the job helps improve the final report.

Practical examples

• Getting new hires up to speed. A new engineer could ask Rovo, "Give me the complete technical rundown of Project Chimera." Within minutes, they could have a single document summarizing the architecture, key development epics, and all the important documentation.

• Keeping projects on track. A product manager could ask, "What were all the major decisions and roadblocks for the Q3 launch?" Rovo could pull together a clean timeline from scattered meeting notes, status updates, and Slack messages.

• Checking out the competition. A strategist could ask it to "summarize how competitors are using AI in their tools." Rovo could gather data from connected third-party sources and internal analyses to create a handy competitive brief.

Limitations of Rovo Deep Research Reasoning and an alternative for action-oriented teams

Rovo is clearly a smart tool for research, but it was designed with some specific limits in mind. These become pretty obvious if your team's goal is to automate work, especially when it involves customers.

Where Rovo Deep Research Reasoning falls short

• It’s for research, not resolution. The end product of Rovo's work is a report. It’s brilliant at giving you a summary, but it can’t take the next step. It can’t resolve a customer support ticket on its own, figure out an IT problem, or look up an order status and update it. It’s built to analyze things passively, not solve them actively.

• It’s a commitment to the Atlassian world. To get the most out of Rovo, your company really needs to be all-in on Atlassian's products. Pulling in knowledge from tools outside that bubble can be awkward and not as effective. The setup also has that classic enterprise software feel; it's not something you can just switch on and start using in an afternoon.

• The price is a bit of a mystery. Rovo isn’t a product you can buy off the shelf. It comes bundled with Atlassian's top-tier Premium and Enterprise Cloud plans. There's no straightforward pricing for a team that just wants to try it out, making it a pretty big investment from day one.

An alternative built for action: eesel AI

If your main goal is to turn all that company knowledge into automated actions instead of just reports, a tool like eesel AI was built from the ground up for that exact purpose.

• Go live in minutes, not months. Forget about long, drawn-out enterprise setups. eesel AI is completely self-serve. You can connect your help desk, whether it's Zendesk or Freshdesk, and all your other knowledge sources with simple integrations. You can have a working AI agent up and running in minutes.

• Connect all your knowledge, no matter where it lives. While Rovo is most comfortable in the Atlassian playground, eesel AI instantly hooks into the tools your team already relies on. Whether your answers are hiding in Google Docs, Notion, Slack, or past support tickets, eesel AI pulls it all together into one brain for its AI to use.

• Automate workflows, don't just write about them. eesel AI's agents are designed to act. Once they find an answer, they can triage tickets, look up live order information from a platform like Shopify, escalate tricky issues to the right person, and solve customer problems from start to finish.

• Test it out first and know what you're paying for. eesel AI has a simulation mode that lets you test your AI on thousands of your past tickets before it ever talks to a real customer. This gives you a solid idea of how it will perform. And unlike Rovo's bundled pricing, eesel AI has clear, flexible plans without any hidden per-resolution fees.

Feature	Atlassian Rovo Deep Research	eesel AI
Primary Goal	Create in-depth research reports	Automate and resolve support issues
Core Action	Learns and summarizes information	Learns and takes action (answers, tags, escalates)
Setup Time	Weeks to months (Enterprise deployment)	Minutes to hours (Self-serve)
Knowledge Sources	Best with Atlassian tools (Jira, Confluence)	Help desks, wikis, chat tools (100+ integrations)
Pricing Model	Bundled with high-tier Atlassian plans	Transparent, predictable monthly/annual plans

Rovo Deep Research Reasoning: From deep research to immediate action

The Rovo Deep Research Reasoning engine is a solid, transparent tool for any team that needs to do complex internal digging, especially if they’re already living and breathing Atlassian. Its real value is in turning messy data into clean knowledge that can guide big decisions.

But at the end of the day, it's a tool for learning, not a tool for doing. It creates reports; it doesn't drive automation in the moment.

For support, IT, and ops teams who need to close the gap between finding information and actually using it, eesel AI is a much better fit. It’s designed from the start to resolve issues, not just report on them. It turns your company's scattered knowledge into a tool for getting things done right now.

Ready to turn your scattered knowledge into automated resolutions? Sign up for a free eesel AI trial and see how quickly you can automate your frontline support.