Rovo Agent or Custom Forge App? – A Roadmap for Your Atlassian AI Strategy

The introduction of Rovo has reshuffled the deck in the Atlassian ecosystem. For a long time, the answer to the question “Can we extend Jira with this feature?” was almost always: “Yes, we’ll build or commission a Forge app.”

But with the arrival of specialized AI agents that process natural language, companies now face a new choice: Do you still need to write actual code for your next automation, or is a well-configured agent enough?

Let’s look at when Rovo delivers its value and when the Forge architecture remains indispensable.

An Overarching Goal: Efficiency

Before we compare the two approaches, it should be clarified once more how they fundamentally differ:

Rovo Agents are essentially “intelligent assistants” for specific tasks. They are based on Large Language Models (LLMs) and leverage the context of your entire Atlassian environment. For example, they are designed to find information, synthesize content, and execute tasks based on natural language instructions.
Forge Apps are classic software extensions for Jira, Confluence, and the like. They run on Atlassian’s serverless infrastructure and follow deterministic logic. Here, the code determines exactly what happens—without the interpretative leeway of LLM-based AI solutions.

When a Rovo Agent Serves Its Purpose

The biggest advantage of Rovo is the speed of deployment. Once a team gets the hang of it, setting up a specialized agent is usually a straightforward exercise. An agent is the right solution for tasks that previously required human cognitive effort but were too variable for rigid code rules. The following scenarios illustrate this.

Knowledge Management and Synthesis

When it comes to summarizing information from dozens of Confluence pages or Jira tickets, Rovo is hard to beat. An agent can draft project status reports, identify conflicting requirements, or personalize onboarding materials for new team members—the possibilities are diverse.

Ad-hoc Support

Do you want an assistant to check your tickets for compliance with internal writing guidelines? Or create a draft for a customer response based on a technical bug report? Such tasks, which previously would have required complex scripts, can be configured with Rovo in minutes.

Decision and Contextual Assistance

Rovo also proves its worth when it comes to preparing decisions or making complex relationships quickly graspable. Instead of just providing information, an agent can contextualize and weigh it, putting it into an action-oriented format. For instance, prioritization can be supported by sorting relevant tickets according to business impact, risks, or dependencies. Similarly, an agent can generate different views of the same content—for example, for management, product owners, or development teams.

Exploration and Knowledge Access

Another field of application for Rovo is exploratory access to knowledge. Especially in long-standing Atlassian instances, relevant information is often spread across numerous projects, pages, and tickets. Here, an agent can serve as an intelligent entry point: it doesn’t just answer questions based on individual documents but establishes connections, refers to relevant content, and identifies the right contact persons. Typical use cases include searching for already solved problems or identifying experts on a specific topic.

When a Forge App Remains Indispensable

Despite the impressive capabilities of AI, there are relatively clearly defined limits: they are reached when specialized agents cannot cope with the complexity of a requirement or when the “creative” nature of an LLM introduces risks. Whenever reliability, structure, and technical integration are paramount, the code-based structure of Forge is required.

Deterministic Processes and Compliance

In areas where an error is not an option, AI must not “guess” what a user wants: as soon as a process leaves no room for interpretation—such as financial approvals or security-critical workflows—an LLM-based approach is problematic and risky. A Forge app, on the other hand, guarantees that Step B always follows Step A exactly—precisely defined and reproducible. It ensures, for example, that a ticket only moves to the next status once all mandatory fields are set, necessary approvals are present, and external checks have been successfully completed.

Complex User Interfaces

Rovo interacts primarily through chat interfaces or simple interaction patterns. However, as soon as use cases require independent, structured user guidance, Forge comes into play. If your solution needs to integrate its own dashboards, specialized input masks, or multi-step forms directly into the Jira or Confluence interface, Forge offers the necessary flexibility with Custom UI and the UI Kit. Users capture structured data, go through defined steps, and receive direct feedback. Such an interface can be implemented precisely with Forge, including all business rules.

Data Logic and Automation

Another area where Forge plays to its strengths is the implementation of complex logic and automated background processes. This includes rule-based decisions, such as the automatic assignment of tickets based on a skill matrix, as well as the processing of large amounts of data or event-driven architectures with triggers and webhooks. A Forge app can, for example, continuously check whether SLA limits are being violated and escalate automatically if necessary. Such mechanisms work in the background, follow clear rules, and must be traceable at all times.

Deep System Integration

While Rovo can process information and establish connections, the stable connection of external systems is a classic domain of Forge. As soon as data needs to flow reliably between Atlassian products and third-party systems, an app integration is required. This applies to ERP, CRM, or legacy systems as well as transactional processes where data must not only be read but also written and confirmed. A Forge app can, for example, synchronize your Jira tickets with an ERP system, perform validations, and handle errors cleanly. Such integrations require clear interfaces, defined states, and controlled processes—all aspects that cannot be sensibly left to an agent.

The Synergy: The Best of Both Worlds

It is important to understand that Rovo and Forge are not in competition with each other. On the contrary: they actually complement each other quite well. While Rovo acts as an intelligent assistant that understands context and simplifies user interactions, Forge takes on the role of the reliable execution instance in the background. Or to put it another way: Rovo decides what should be done; Forge ensures it happens correctly.

The keyword is Forge Actions: your development teams have the opportunity to provide customized “skills” using Forge, which can be used specifically by Rovo agents. In this way, the flexibility of AI can be combined with the reliability of a classic software architecture.

An example is a Rovo agent that answers your questions about current stock levels. While the agent interprets your request and prepares the result in an understandable way, a Forge app handles the secure connection to the ERP system, retrieves the data, and ensures its correctness.

Another scenario: you formulate a request in natural language to create a ticket for a critical security problem. Rovo understands the intention, supplements missing information, and structures the request. A Forge app then takes over the creation of the ticket—including correct classification, validation, and integration into existing workflows.

For another example of Rovo and Forge working hand in hand, check out how a Rovo Agent and Automation can be combined to format phone numbers in Jira—a practical use case from the Atlassian community that illustrates the synergy nicely.

This creates an architecture in which the AI does not work in isolation but specifically accesses robust, clearly defined functions. Rovo becomes the intuitive interface for users, and Forge becomes the stable foundation for processes and integrations. The future, therefore, does not lie in choosing one of the two technologies, but in their targeted integration.

Do you want to take your Atlassian environment to the next level with AI? Whether you need support configuring individual Rovo agents or are planning a highly specialized Forge app—our experienced Atlassian professionals will help you from strategic planning to implementation.