Rovo is designed for transparency

• How Rovo Agents use AI

  Rovo Agents are powered by large language models developed by OpenAI and Google, as well as a combination of open-source large language models (including the Llama series) and other machine learning models. These models include OpenAI’s GPT series of models and Google’s Gemini series of models.

  Rovo Agents use these models to provide functionalities to analyze and generate responses to prompts in natural language, and provide relevant responses from Atlassian and connected third-party products.

  These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text, based on the data that they have been trained on.

  Read more about the capabilities of OpenAI’s models and Google’s models. For more information on open-source language models, see information on the Llama series.

• About Rovo Agents

  With Rovo, we provide a number of out-of-the-box Agents, ready to use for a variety of tasks like, helping with decisionmaking, publishing knowledge documentation, and tidying up or organizing work items in Jira. Agents are specialized AI teammates that can assist human teams with moving work forward quickly and effectively.

  You can:

  • Create your own Agents using natural language and customize their instructions, knowledge, and actions
  • Create your own Agents using code (Forge) to have more advanced integrations with your organization’s internal tools
  • Install Agents via apps by developed by third-party Marketplace developers, customers, and Atlassian partners, which are typically more sophisticated Agents created using code (Forge).

  We believe that Rovo Agents work best in scenarios where:

  • A repetitive or formulaic task that would be easily automated through an Agent. Read more about automations
  • Generating ideas or draft content to work from
  • Performing fast searches for specific information

• Limitations and considerations when using Rovo Agents

  It’s important to remember that because of the way that the models used to power Rovo Agents work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable.

  For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete.

  We’ve found that Rovo Agents are less useful in scenarios where:

  • You need current and accurate information about people, places and facts.
  • You need information from your workspace that you don’t have access to within your workspace.
  • You need very consistent and regular output that is not subject to any variation as answers can vary for a number of reasons including user permissions.
  • Humans aren’t involved. Agents work best with a human to verify or guide the desired output.

  For this reason, we encourage you to think about the situations when you use Rovo and review the quality of the responses you receive before sharing them with others.

  You might also want to think about:

  • Being as specific as possible in what you ask Rovo to do.
  • Reading best practice guides on writing prompts for Agents.
  • Finding efficient or tested ways to incorporate Agents in your team. You can see how others on your team are using Agents by viewing Agent profiles.

• Your data and Rovo Agents

  We understand you may have questions about how Rovo Agents use your data. This section supplements the information available on our Trust Center.

  We process:

  • Your prompts (inputs) and responses (outputs).
  • Context from your instance relevant to your prompt, such as a Confluence page, Jira work item or a document from a connected third-party product. If you use Agents in Rovo Chat via the Chrome browser extension, this includes the public webpage or Google Doc you have open.
  • Data about how you interact with our features, such as clickstream data and the people you work with.
  • Feedback you choose to provide about this feature, including any prompts or responses you choose to share as part of your feedback.

  When it comes to your data, Rovo Agents apply the following measures.

  Your prompts (inputs) and responses (outputs):

  • Are not available to other customers.
  • Are not sent to any third party LLM provider other than OpenAI or Google.
  • Are not stored by any third-party LLM provider.
  • Are not used to improve any large language models.
  • Are retained by Atlassian for 30 days for safety and security purposes.
  • OpenAI is a subprocessor on our List of Subprocessors. They do not use your inputs and outputs for any purpose besides processing your request.
  • This feature follows the permissions in your instance. For example, if you do not have access to a certain Confluence page, you will not be suggested content from that page in the response you receive. If you do not want your content to be available in responses to other users in your instance, please work with your org admin to ensure your permissions are set appropriately.

  The details above apply to Agents that are provided by Atlassian. For more information about Agents provided by Atlassian, please see Rovo data, privacy and usage guidelines | Rovo | Atlassian Support. For information about Agents provided by third parties, please reference the Vendor Terms provided by those third parties for their apps.

• How Rovo Customer Service uses large language models

  Rovo Customer Service is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models.

  Rovo Customer Service uses these models to analyze and generate natural language within our products.

  These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated through predicting the most probable next word or text based on the data that they have been trained on.

  Read more about the capabilities of OpenAI's models, Google's models and Anthropic's models. For more information on open-source language models, see information on the Llama series and the Phi series.

• Use cases for Rovo Customer Service

  Rovo Customer Service is the AI agent capability in Customer Service Management (CSM). It allows organizations to configure their own AI agent that can answer customer queries and requests in seconds, and retrieve answers or automate actions to resolve requests. Organizations can continually monitor and enhance agent effectiveness. Admins can also use Rovo Customer Service to analyze past support interactions, identify gaps in their agent's knowledge, and generate suggested knowledge content for admin review using the CSM Optimize feature.

  We believe that Rovo Customer Service works best in these scenarios:

  • Your organization has a well-structured knowledge base.

  • Your organization has a meaningful portion of customer queries that can be resolved through knowledge articles or simple actions.

  • When using CSM Optimize, your organization has past support interactions that are relevant and appropriate to use for improving the AI agent's knowledge. Having at least 500 past support interactions is required to use the automated optimization.

  • Your organization is willing to continuously evaluate and use available configuration features in the app to refine the AI agent's performance.

• Considerations when using Rovo Customer Service

  It's important to remember that because of the way that the models used to power Rovo Customer Service work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable.

  For example, the responses that you and your end users receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete.

  We've found that Rovo Customer Service is less useful in scenarios where:

  • You need current and accurate and up to date information about people, places and facts.

  • You need Rovo Customer Service to have access to information that isn't readily available to you (for example, connected knowledge base) to properly answer your request.

  • Your connected knowledge base for Rovo Customer Service is outdated or contains wrong information, or does not contain enough information.

  • Your instructions or knowledge base provided to Rovo Customer Service contains conflicting information.

  • You should review any suggested knowledge content created by CSM Customize before publishing it internally, or making it available to Rovo Customer Service.

  Rovo Customer Service should not be used in scenarios that are outside of customer service, or for any purpose that would violate Atlassian's Acceptable Use Policy.

  For this reason, we encourage you to think about the situations when you use Rovo Customer Service and review the quality of the responses you receive before enabling Rovo Customer Service for your end users.

  You might also want to think about:

  • Being as specific as possible in what you ask Rovo Customer Service to do.

  • Ensure the knowledge sources you provide to Rovo Customer Service are reviewed and suitable for end users. We strongly recommend removing internal-only content, confidential information and simplifying language to deliver a clear, accurate customer experience.

  • Regularly review end-user conversations with Rovo Customer Service, and make improvements where needed - whether by updating the knowledge base, adjusting actions, or adding guidance.

• Your data and Rovo Customer Service

  We understand you may have questions about how Rovo Customer Service uses your data. This section supplements the information available on our Trust Center.

  We process:

  • Your prompts (inputs) and responses (outputs).

  • Context from your instance relevant to your prompt, such as the current conversation, connected knowledge base, available actions, and your admin-configured guidance - to deliver relevant responses.

  • Past support interactions that admins choose to use for CSM Optimize, and suggested knowledge content generated from those interactions.

  • Data about how you interact with our features, such as clickstream data and the people you work with.

  • Feedback you choose to provide about this feature, including any prompts or responses you choose to share as part of your feedback.

  When it comes to your data, Rovo Customer Service applies the following measures:

  Your prompts (inputs) and responses (outputs):

  • Are not available to other customers.

  • Are not sent to any third party LLM provider other than OpenAI, Google, or Anthropic on AWS Bedrock.

  • Are not stored by any LLM provider.

  • Are not used to improve any large language models.

  OpenAI, Google, and Anthropic on AWS Bedrock are subprocessors on our List of Subprocessors. They do not use your inputs and outputs for any purpose besides processing your request.

  This feature follows the permissions in your instance. Rovo Customer Service only has access to knowledge and support-interaction data that you manually connect, select, upload, or otherwise make available to Rovo Customer Service.

  Admins can choose whether to use CSM Optimize features and can review, edit, accept, reject, or delete suggested knowledge content before it is published or used with end users.

  Your conversation history will be retained in your Rovo Customer Service instance and available to admins, while you maintain an active license, unless you manually delete it.

  Admins have access to an evaluation capability, which enables the admin to upload a predefined data set, and use that data set to evaluate the performance of the AI agent. You can delete evaluations and conversations that are generated with the data set. When an evaluation is deleted, all metrics for the evaluation, will also be deleted.

• Read more about Rovo

  Discover more about using Atlassian AI

  Improve your agent's performance based on past support interactions

• How create incident uses AI

  Create incident with AI using Rovo is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models.

  Rovo uses these models to analyze and generate natural language within our products.

  These models generate responses based on your input and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they've been trained on.

  Read more about the capabilities of OpenAI’s models, Google’s models and Anthropic’s models. For more information on open-source language models, see information on the Llama series.

• Use cases for create incident with AI

  When escalating one or more alerts or alert groups to an incident in Jira Service Management, create incident with AI uses Rovo to quickly pre-populate all contextual information for you to review as part of the incident creation process. This allows users to quickly understand the context of the incident created from those alerts or alert groups, and review and confirm pre-populated information including the title, description and priority of the alert when escalating it to an incident.

  We believe that create incident with AI works best in scenarios where:

  • The alerts that you are escalating to an incident include title and descriptions in English.
  • The alerts that you are escalating to an incident have lengthy descriptions.
  • When you are creating an incident from more than one alert.

• Considerations when using create incident with AI

  It’s important to remember that because of the way that the models used to power create incident with AI work, these models can sometimes behave in ways that are inaccurate, incomplete or unreliable.

  For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete.

  We’ve found that create incident with AI is less useful in scenarios when:

  • You need current and accurate information about people, places, and facts.
  • The alerts that you are escalating to an incident include their title or description (or both) in a language other than English.
  • The alerts that you are escalating to an incident contain only limited details.

  For these reasons, we encourage you to think about the situations when you use Rovo and review the quality of the responses you receive before sharing them with others.

  To get the most useful results we suggest being as specific as possible in what you ask Rovo to do.

• Your data and create incident using AI

  We understand you may have questions about how create incident with AI uses your data. This section supplements the information available on our FAQ page.

  We process:

  • Your prompts (inputs) and responses (outputs).
  • Context from your instance relevant to your prompt, such as the Jira Service Management alert description, title, and priority.
  • Data about how you interact with our features, such as clickstream data and the people you work with
  • Feedback you choose to provide about this feature, including any prompts or responses you choose to share as part of your feedback.

  When it comes to your data, create incident with AI applies the following measures:

  Your prompts (inputs) and responses (outputs):

  • Are not available to other customers.
  • Are not sent to any third party LLM provider other than OpenAI, Google, or Anthropic on AWS Bedrock.
  • Are not stored by any LLM provider.
  • Are not used to improve LLM models.
  • All third-party LLM providers are subprocessors and listed as so on our Subprocessors page. They do not use your inputs and outputs for any purpose besides processing your request.
  • This feature follows the permissions for alerts in your instance. Only agents that have permission to view the alert and escalate it to an incident will see suggestions by Rovo for filling the details for the incident getting created.

• How Chart insights uses AI

  Chart insights is powered by large language models developed by OpenAI, Google, and Anthropic, as well as a combination of open-source large language models (including the Llama series and Mixtral series) and other machine learning models. These large language models include OpenAI's GPT series of models, Google's Gemini series of models, and Anthropic's Claude series of models.

  Rovo uses these models to analyze and generate natural language within our products.

  These models generate responses based on your inputs and are probabilistic in nature. This means that their responses are generated by predicting the most probable next word or text based on the data that they have been trained on.

  Read more about the capabilities of OpenAI's models, Google's models and Anthropic's models. For more information on open-source language models, see information on the Llama series.

• Use cases for Chart insights

  Chart insights uses AI to help speed up your understanding of data in any chart in Atlassian Analytics. It does so by using the dashboard title, chart title, and chart data (including column headers and row values) to generate a natural language summary of that chart and its data. It will also aim to identify any trends or anomalies to provide you with certain insights into that chart.

  We believe that Chart insights work best in scenarios where:

  • Charts have many rows of data.
  • Charts have a dashboard title.
  • Charts have column headers.
  • Charts have values in all of the rows and columns.

  Bar charts, line charts, and bar-line charts work best with this feature since they typically have trends, dates, and many rows of data.

• Considerations when using Chart insights

  It's important to remember that because of the way that the models used to power Chart insights work, these models can sometimes behave in ways that are inaccurate, incomplete, or unreliable.

  For example, the responses that you receive might not accurately reflect the content that they are based on, or include content that sounds reasonable but is false or incomplete.

  We've found that Chart insights is less useful in scenarios where:

  • You have charts with one or just a few rows of data.
  • You have charts that are of the single value type.
  • You have charts missing titles, axis-labels, and column headers.

  For this reason, we encourage you to think about the situations when you use Rovo and review the quality of the responses you receive before sharing them with others.

  You might also want to think about:

  • Double checking the accuracy of the insights with other users who may have more context on the specific data displayed in the chart.
  • Taking into account that Rovo is only using the context of a single chart and not the entire dashboard when providing a response.

• Your data and Chart insights

  We understand you may have questions about how Chart insights uses your data. This section supplements the information available on our FAQ page.

  We process:

  • Your prompts (inputs) and responses (outputs).
  • Context from your instance relevant to your prompt, such as the data in your chart.
  • Data about how you interact with our features, such as clickstream data and the people you work with.
  • Feedback you choose to provide about this feature, including any prompts or responses you choose to share as part of your feedback.

  When it comes to your data, Chart insights applies the following measures.

  Your prompts (inputs) and responses (outputs):

  • Are not available to other customers.
  • Are not sent to any third party LLM provider other than OpenAI, Google, or Anthropic on AWS Bedrock.
  • Are not stored by any LLM provider.
  • Are not used to improve LLM models.
  • All third-party LLM providers are subprocessors and listed as so on our Subprocessors page. They do not use your inputs and outputs for any purpose besides processing your request.
  • This feature only uses information from the dashboard you have access to and have requested insights for.