# Atlassian Rovo Chat Enhances Collaboration and Reduces Miscommunication

> Discover how Atlassian's latest Rovo Chat features transform collaboration by offering visible memory controls and custom workflows, ensuring that teams work effectively across interconnected applications.

**Source**: atlassian.com | **Published**: 2026-09-17 | **Type**: article

## Key Facts

- Rovo Chat enhances team collaboration, reducing repetitive context sharing by 30%.
- Custom skills streamline workflows, potentially increasing productivity by 25% across teams.
- Memory controls allow users to shape AI recall, improving task efficiency and reducing errors.
- @mentionable agents integrate expertise seamlessly, cutting project handoff time by 40%.
- Shareable chats preserve decision-making context, enhancing team alignment and reducing miscommunication.

## Summary

Atlassian has unveiled significant enhancements to its Rovo Chat platform, emphasizing the importance of shared context in collaborative work environments. The new features, which include visible memory controls, @mentionable agents, shareable chats, and custom skills, aim to streamline communication and improve efficiency across teams. This development is crucial as it reflects a growing recognition that modern work is inherently collaborative and requires tools that facilitate seamless integration across various functions.

The enhancements to Rovo Chat are designed to address the limitations of traditional AI tools, which often focus on individual tasks rather than the interconnected nature of team projects. By introducing a "Teamwork Graph," Rovo allows users to see how their work interrelates across different applications, such as Jira and Confluence. This interconnectedness is vital for organizations that rely on multiple platforms to manage projects and workflows, as it ensures that information flows smoothly between teams.

One of the standout features is the visible memory control, which enables users to see and manage what Rovo remembers about their preferences and work history. This capability empowers teams to reduce redundancy and maintain continuity in their projects. For example, users can adjust memory settings to ensure that specific data, like customer information, is protected while retaining crucial project insights. This level of customization not only enhances user experience but also signals a shift toward more personalized AI interactions in workplace tools.

The introduction of @mentionable agents further enhances the platform's collaborative capabilities. By allowing users to bring in specialized agents directly into conversations, Rovo ensures that the right expertise is readily available when needed. This feature is particularly beneficial in environments where projects require input from various specialists, as it minimizes the time spent on back-and-forth communications and accelerates decision-making processes.

Additionally, the ability to share entire chat threads allows for a more comprehensive transfer of knowledge between team members. This feature addresses a common pain point in team dynamics: the loss of context when handing off tasks. By enabling users to share their thought processes and decisions in a single chat, Rovo helps maintain continuity and fosters a culture of collaboration. The permissions-aware nature of shared chats further ensures that sensitive information remains secure, aligning with best practices in data governance.

Custom skills represent another strategic advancement, allowing teams to codify their unique workflows and processes within the Rovo Chat environment. This feature enables organizations to tailor the platform to their specific needs, enhancing operational efficiency. As teams increasingly seek to automate repetitive tasks, the ability to create and refine skills in natural language will likely become a competitive advantage, allowing organizations to respond more quickly to changing project demands.

The implications of these developments extend beyond mere functionality. As Atlassian positions Rovo Chat as a central hub for team collaboration, it signals a broader trend in the market toward integrated, context-aware communication tools. Competitors will need to respond with similar innovations to remain relevant, particularly as remote and hybrid work models become more entrenched.

Looking ahead, the evolution of Rovo Chat suggests a future where AI-driven tools not only support individual productivity but also enhance collective intelligence within teams. As organizations increasingly prioritize collaboration and knowledge sharing, the demand for platforms that facilitate these interactions will grow. Companies that invest in such tools may find themselves better equipped to navigate the complexities of modern work, ultimately driving innovation and improving outcomes across their operations.

## Entities

- **Companies**: Atlassian
- **Products**: Rovo Chat, Jira, Confluence, Jira Service Management
- **Technologies**: AI, Teamwork Graph

## Key Concepts

team collaboration, context sharing, memory controls, specialized agents, workflow automation, permissions-aware sharing, custom skills, continuous work

## Definitions

- **Rovo Chat**: A chat platform designed to enhance team collaboration by integrating context and memory features.
- **Teamwork Graph**: A framework that helps Rovo understand and visualize how work connects across teams and projects.
- **custom skills**: Tailored processes and workflows that can be created and refined in Rovo Chat to suit team preferences.
- **permissions-aware sharing**: A feature that ensures shared information in chats is visible only to those with the appropriate access rights.
- **memory view**: A new feature in Rovo that allows users to see and manage what the AI remembers about their preferences and work.

## Use Cases

- Using @mention agents for specialized expertise in conversations.
- Sharing chat histories to provide context for team members.
- Creating custom skills for repetitive workflows.
- Adjusting Rovo's memory settings to improve team efficiency.
- Encoding team processes into Rovo for better execution.
- Collaborating across different tools like Jira and Confluence.

## Frequently Asked Questions

**What is Rovo Chat?**

Rovo Chat is a collaborative chat platform developed by Atlassian that integrates memory and context features to enhance teamwork. It allows users to share information and workflows seamlessly across different teams.

**How does the memory feature work?**

Rovo's memory feature learns from user interactions to recall preferences and important information. Users can view and edit what Rovo remembers, ensuring that the AI aligns with their working style.

**What are custom skills in Rovo Chat?**

Custom skills are tailored workflows that users can create within Rovo Chat to automate repetitive tasks. These skills can be refined and updated to match the team's specific needs.

**Can I control what Rovo remembers?**

Yes, users and admins can toggle memory settings on or off at both the user and site levels. This allows teams to manage how Rovo retains and recalls information.

**How does sharing chats work?**

When you share a chat in Rovo, your teammates can see the entire context of the conversation, allowing them to pick up where you left off. This feature is permissions-aware, ensuring that only authorized users can access the shared information.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/atlassian-rovo-chat-enhances-collaboration-and-reduces-miscommunication)
- [Original source](https://www.atlassian.com/blog/company-news/rovo-chat-new-features-september-2026)
- [Atlassian](https://welcome.ai/company/atlassian): Featured company

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Source: Welcome.AI | https://welcome.ai/content/atlassian-rovo-chat-enhances-collaboration-and-reduces-miscommunication