Rovo Agents: Your New AI Teammates

Atlassian’s Rovo Agents are AI teammates embedded in the software you use every day. These agents can handle routine tasks, analyze data, and provide insights, all within Jira, Confluence, and other Atlassian apps. The result is a new era of “agentic AI” in the workplace, where AI works alongside people to automate work and boost productivity. Organizations are looking to Rovo Agents as the future of work automation, transforming how teams operate and freeing humans to focus on higher-value activities.

Out-of-the-Box vs. Custom Rovo Agents

• Out-of-the-Box Rovo Agents: Atlassian provides a library of pre-built AI agents ready to deploy in your workflows. These come included for customers on Premium and Enterprise Cloud plans (with Standard support on the way), so you can start using AI assistants without extra cost. Out-of-the-box agents cover common scenarios: for example, a code reviewer that checks pull requests, or a support agent that helps answer IT tickets, all configured by Atlassian and instantly usable.

• Custom Rovo Agents: Every team has unique needs, and Rovo allows you to create your own AI agents to meet them. You can build custom agents through no-code/low-code tools like Atlassian’s new Rovo Studio, which provides a user-friendly interface to design AI workflows without programming. For more complex requirements, developers can use the Forge platform to code bespoke Rovo Agents with tailored prompts and actions. This means you can integrate AI deeply into your specific processes, whether by configuring an agent in a few clicks or developing a custom solution from scratch.

Key Use Cases for Rovo Agents

Rovo Agents are designed to tackle specific tasks and make life easier across different domains. Two notable examples highlight their potential:

• Code Reviewer Agent: This AI agent acts as an intelligent pull request reviewer for development teams. When a developer submits code in Bitbucket, the Code Reviewer Agent can automatically analyze the changes against project requirements (from Jira user stories) and coding best practices. It flags potential issues, like missing requirements, style inconsistencies, or performance concerns, before a human ever looks at the code. By catching bugs and omissions early, this agent speeds up code reviews and helps maintain high code quality without overburdening your engineers. Developers get faster feedback and can merge code with greater confidence that it meets both technical and business criteria.

• Root Cause Analysis Agent: For IT operations and support teams, the Root Cause Analysis agent is a game changer when incidents occur. This agent automatically sifts through monitoring alerts, recent code deployments, support tickets, and other DevOps data to pinpoint what might be causing an outage or problem. Instead of manually combing through logs and change histories, your team can get an AI-assisted analysis of likely root causes within seconds. The agent might, for example, correlate a spike in errors to a specific software deployment or configuration change. By quickly identifying the source of issues, the Root Cause agent helps teams resolve incidents faster and minimize downtime.

Building Rovo Agents: No-Code vs. Developer Approaches

• No-Code/Low-Code Creation: Atlassian’s Rovo Studio makes it possible to create custom AI agents with little to no coding. Through a guided interface, users can define an agent’s purpose, the data it should use, and the actions it can perform, all by configuring settings and writing natural language prompts. This means even non-technical team members can spin up an AI agent tailored to their workflow. For example, a team lead could configure an agent to automatically summarize weekly Jira updates, without writing a single line of code. Rovo Studio handles the heavy lifting, so you get a working agent quickly.

• Developer-Built Agents (Forge): For specialized needs or deeper integrations, developers can turn to Atlassian’s Forge platform to build Rovo Agents programmatically. Using Forge, a developer can define a Rovo Agent in code, specifying its behavior with custom prompts and connecting it to various data sources or actions beyond the standard options. This opens up advanced possibilities, such as an agent that interfaces with external systems or performs multi-step operations unique to your business. The developer approach requires coding expertise, but it offers maximum flexibility. Both methods ultimately complement each other: no-code tools empower any user to create simple agents, while the developer route enables richer, highly customized AI assistants when needed.

Identifying Ideal Processes for AI Automation

How do you decide which of your workflows would benefit most from a Rovo Agent? Start by looking for repetitive, time-consuming tasks in your organization. Routine processes that follow a defined set of steps, like updating tickets, generating reports, or checking code for standards, are prime candidates for automation by an AI agent. Also consider tasks that involve large amounts of data or documentation, where an AI could quickly analyze or summarize information. The best opportunities often lie in areas where employees spend a lot of effort on manual, procedural work that doesn’t require significant creative judgment. Engage with your teams to pinpoint their pain points: if there’s a process that people find tedious or that creates a bottleneck, an AI agent might be able to handle it or at least assist. It’s helpful to start with a small pilot: implement an agent for a well-defined task and measure the impact, before expanding to other processes.

ROI of Rovo Agents and Automation

• Time Savings: One of the clearest returns on investment from AI agents is the hours of manual work they save. By automating tasks, skilled employees reclaim time to focus on more strategic or complex work. You can quantify this by estimating how many hours per week an agent saves and multiplying by the value of that time.

• Faster Cycle Times: Rovo Agents can dramatically speed up workflows. They may help resolve support tickets faster, complete deployments with fewer delays, or shorten development cycles by catching issues early. Faster task completion means projects move forward sooner, which can translate into faster time-to-market and cost savings.

• Improved Quality & Consistency: AI agents perform tasks consistently every time, reducing human errors and variability. A Code Reviewer Agent that never tires can enforce coding standards uniformly, potentially preventing bugs that would cause costly rework. In customer support, an AI agent that provides instant, accurate answers improves service quality and customer satisfaction, which has long-term financial benefits.

• Low Marginal Cost: Once deployed, an AI agent can often operate at scale with minimal additional cost. If you already have Rovo included in your Atlassian plan, using more of its built-in agents doesn’t usually require significant new expenditure, yet each agent can contribute tangible efficiency gains. When calculating ROI, consider that the incremental cost of running an extra agent might be very low compared to hiring additional staff for the same workload.

Change Management for AI Adoption

Introducing Rovo Agents into your organization isn’t just a technical implementation; it’s a change that affects your people and processes. Effective change management is crucial to ensure your team embraces these new AI teammates. Start by clearly communicating the purpose and benefits of the agents to all stakeholders. Team members should understand that the agents are there to assist and augment their work, not to replace them. This helps alleviate fears and builds trust in using AI. Next, provide training and guidance on how to interact with the agents: for example, show developers how to interpret suggestions from the Code Reviewer Agent, or teach support staff when to leverage the Root Cause Analysis Agent during an incident. It’s wise to introduce AI agents gradually: perhaps roll out one or two agents as a pilot and gather feedback. Identify champions or power users who can showcase quick wins and help colleagues learn from those successes. By involving your team early, addressing concerns, and iterating based on feedback, you’ll foster a culture that welcomes automation. Over time, people will come to trust the AI agents as reliable coworkers and integrate them seamlessly into daily work.