🌱 Two ways Rovo helps the Atlassian Learning team prioritize their expertise

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Over in the Training & Certification group, the Atlassian Learning team has been sharing how we’re handing off repetitive, straightforward tasks to Rovo so we can focus on higher-level work. We thought this group might like to see the latest.

In How a non-dev created a useful Rovo agent, I share a first-person account of how I went from a Rovo Search and Chat user to building an agent I now use all the time (even though I’m not a developer). The article shares how I save time and energy by handing off repetitive work to a Rovo agent. You’ll see the 4-step agent development framework I used, and details about my agent.

And, in How Rovo Deep Research supports our team’s expertise, our team’s Rovo champion, Michelle Cacciapaglia, shows how she led the team in developing a repeatable use case for Rovo Deep Research that cuts through information overload and saves time. Now, the team can focus on the creative parts of course design. You’ll also see practical tips and a refined prompt you can adapt for your own research needs.

Have you been building agents or using Rovo Deep Research? Tell us how in the comments! 🌱

And see more from the Atlassian Learning team in our Behind the scenes with Atlassian Learning series. 

Read the whole series

[Febin Philipose]

Hi Atlassian Community, 

It is fascinating to see how Rovo Agents are transforming the way we handle repetitive operations. I want to share a usecase I am currently developing to automate capacity tracking and eliminate manual spreadsheet analysis.

I am building a Rovo Agent designed to monitor individual and team utilization against planned capacity by analyzing active worklogs. The configuration process has been remarkably seamless:
Persona Assignment: Defined the agent’s role specifically as a Data Analyst.
Context & Mapping: Provided data on planned capacity and instructed the agent to cross-reference worklog authors with actual time logged.
Business Logic: Configured a rule to evaluate planned vs. logged time, automatically flagging any individual or team falling below an 80% utilization threshold.

I am currently refining the prompt logic and data outputs to ensure they meet the formatting standards required for senior leadership reporting. However, even in this development phase, it has significantly reduced the administrative overhead of manual tracking.

I would love to connect with anyone else optimizing Rovo Agents for resource management or executive-level data reporting to exchange prompting strategies.

Kind regards,
Febin