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Atlassian partner

Atlassian AI, Rovo and Jira-Confluence agents

Ovyka helps organizations turn Atlassian AI and Rovo into useful, governed use cases: augmented search, summaries, Jira-Confluence agents, controlled automation and team assistance.

The goal is not to add another chatbot. It is to make AI work on your real Jira, Confluence and JSM flows, with reliable sources, respected permissions and measurable outcomes.

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Atlassian AI business cases

What Rovo should help teams do in practice

A strong AI roadmap starts with work gestures that cost time every week: finding, understanding, prioritizing, documenting, routing and acting. Rovo is most useful when those gestures are connected to your real Jira, Confluence and JSM spaces.

Find: retrieve the right context

Value starts when a project lead, support team or product team can find a decision, procedure or history without searching across several spaces.

Learn: understand before acting

Rovo and Atlassian AI can help summarize a Jira ticket, Confluence page, incident or request thread to accelerate decision-making.

Act: trigger action under control

Agents become useful when they propose or execute an action in a clear scope, with known sources, respected permissions and suitable validation.

Rovo ecosystem

Rovo is more than a chat interface

To make the topic clearer and easier to index, the page should name the useful Atlassian building blocks: Rovo Search, Rovo Chat, Rovo Studio, Rovo Agents, Teamwork Graph and Rovo connectors.

Rovo Search

Augmented search across Atlassian work knowledge and connected sources, with strong attention to permissions and documentation quality.

Rovo Chat

Contextual chat to query an issue, page, incident, project or knowledge base without leaving the flow of work.

Rovo Studio

Design space for creating framed agents and automations: objective, instruction, sources, tools, limits and validation rules.

Rovo Agents

Ready-to-use agents or agents adapted to your processes to summarize, classify, organize, recommend or prepare an action.

Teamwork Graph

Context layer connecting people, content, projects, issues and decisions to give AI a usable view of work.

Rovo connectors

Possible connection to third-party tools or internal systems, scoped around permissions, sensitive sources and expected business value.

Rovo Studio and extensions

Rovo Studio, connectors and MCP: frame the key building blocks

Rovo Studio, connectors, MCP access and Atlassian Collections should be framed early because they shape architecture, permissions and the real value of agents.

Rovo Studio

Create agents with a clear objective, maintainable instructions, selected sources and action limits that administrators can understand.

Connectors and sources

Connect Jira, Confluence, JSM and tools such as Microsoft 365, Google Drive, Slack or GitHub only when value and permissions are controlled.

Rovo Dev and MCP

Explore technical use cases carefully: development assistants, MCP access, repositories, spaces and usage rules must remain governed.

Atlassian Collections

Position Rovo within your Atlassian Cloud path: Teamwork, Service, Software or Strategy Collection depending on licenses and real usage.

Licensing, access and adoption

Clarify buying and governance points early

Rovo is not only a feature topic. Access, licenses, credits, the Cloud/Data Center path and adoption indicators should be framed before scale-up.

Rovo licenses, access and credits

Check what depends on Atlassian Cloud plans, collections, user rights and consumption before expanding usage.

  • Identify teams that actually need Rovo Search, Chat, Studio and Agents.
  • Separate pilot, scale-up and administration needs.
  • Connect license decisions to measurable use cases rather than broad activation.

Cloud, Data Center and permissions

Clarify the Cloud/Data Center path, authorized spaces, sensitive sources and residency or compliance constraints.

  • Limit connectors to useful and governed sources.
  • Check permissions before exposing knowledge to AI use cases.
  • Plan exceptions: confidential spaces, customer projects and outdated content.

Rovo adoption measurement

Track real usage, answer quality, exceptions and content to correct before deciding whether the pilot deserves expansion.

  • Measure time saved, knowledge reuse and fewer back-and-forths.
  • Analyze unanswered questions, wrong sources and agents to adjust.
  • Turn pilot feedback into a continuous improvement backlog.

Ovyka

What Ovyka delivers in practice

A buyer needs to know what will be produced, not only what the product can do. Ovyka connects AI strategy, Atlassian administration and field delivery.

Use case scoping

Use case scoping

Prioritize business pain points, teams, volumes, sources and the criteria that will prove pilot value.

Jira-Confluence-JSM review

Jira-Confluence-JSM review

Audit projects, spaces, knowledge bases, permissions, workflows, automations, templates and documentation quality.

AI governance and security

AI governance and security

Define authorized sources, owners, action limits, human validation, sensitive content and publication rules.

Pilot and scale-up

Pilot and scale-up

Configure, test with real teams, measure usefulness, correct data issues and prepare progressive rollout.

Pilot

Pilot Rovo without losing control

The first scope must be useful enough to convince teams, but focused enough to validate permissions, sources, answer quality and human review rules.

  1. 1. Select a verifiable problem

    For example: reduce support triage time, prepare sprint summaries or accelerate project onboarding.

  2. 2. Limit the knowledge scope

    Confluence sources, Jira projects, JSM queues and connectors are selected explicitly instead of opening the entire knowledge estate.

  3. 3. Define what the agent can do

    Suggested reply, comment, summary, ticket, page or recommendation: each action has an expected output and a validation rule.

  4. 4. Measure and correct

    Track adoption, quality, exceptions, content cleanup and new candidate scenarios before expanding.

Use case examples

Jira-Confluence scenarios teams can recognize

The best first pilots are not the most spectacular ones. They reduce a visible friction point, improve decision quality and help teams build confidence in AI usage.

Agile, product and delivery

Backlog grooming and sprint planning

Shorter ceremonies, a clearer backlog and fewer back-and-forths before execution.
Before
Incomplete tickets, duplicates, scattered dependencies, poorly linked epics and old decisions that are hard to find.
With Rovo
Jira-Confluence context summary, missing-information detection, grouping assistance and suggestions for team validation.
Value
Shorter ceremonies, a clearer backlog and fewer back-and-forths before execution.

Support, ITSM and service desk

Request triage and JSM assistance

More consistent qualification, better knowledge reuse and a smoother support experience.
Before
Uneven categorization, manual knowledge-base searches, rewritten replies and escalations that sometimes happen too early.
With Rovo
Contextualized agent or chat to qualify the request, find useful articles, summarize previous cases and prepare a controlled reply.
Value
More consistent qualification, better knowledge reuse and a smoother support experience.

Knowledge and documentation

Confluence AI and knowledge base

A clearer, more reusable Confluence base that is more compatible with reliable AI use.
Before
Outdated pages, duplicates, incomplete procedures and content that is not structured enough to be found or reused.
With Rovo
Support for summarizing, structuring, identifying documentation gaps and turning decisions into usable pages.
Value
A clearer, more reusable Confluence base that is more compatible with reliable AI use.

PMO, managers and new joiners

Onboarding and faster project context

Faster context acquisition and less dependency on already-solicited experts.
Before
A new joiner depends on meetings, scattered links and historical tickets to understand who does what and why.
With Rovo
Discovery path based on Confluence spaces, Jira tickets, decisions, glossaries, owners and active risks.
Value
Faster context acquisition and less dependency on already-solicited experts.

Run, incidents and operations

Incident, post-mortem and follow-up actions

More actionable retrospectives and better-tracked corrective actions.
Before
Incomplete timelines, scattered causes, forgotten post-incident tasks and documentation that is not updated.
With Rovo
Incident summary, decision extraction, PIR preparation, Jira task generation and matching with existing documentation.
Value
More actionable retrospectives and better-tracked corrective actions.

Leadership and portfolio

Executive steering and summaries

Better-prepared decisions, with less time spent reconstructing context.
Before
Project information exists, but must be manually consolidated before a committee or decision.
With Rovo
Summary of risks, blockers, open decisions, critical requests and indicators from relevant Jira-Confluence sources.
Value
Better-prepared decisions, with less time spent reconstructing context.

Atlassian Rovo

Rovo Search, Chat, Studio and Agents to turn knowledge into action

Rovo brings together several usage families: search across work knowledge, contextual chat, agent creation with Rovo Studio, connectors and specialized agents. Atlassian resources notably show examples around ticket organization, release notes, onboarding, support and automations.

Ovyka’s role is to translate these capabilities into use cases adapted to your organization: which spaces, which projects, which permission rules, which validations and which value metrics.

View the official Rovo page Explore Rovo use cases

Connected search

Rovo helps teams use work knowledge across Atlassian applications and connected sources, without removing the need for a clean taxonomy.

Contextual chat

Chat becomes more useful when it answers against a precise Jira, Confluence or JSM context: ticket, page, project, incident, procedure or history.

Specialized agents

Agents can support targeted tasks: organize tickets, review a page, prepare a support reply or launch an action under validation.

Jira-Confluence agents

A useful agent is a framed agent

A Rovo agent should be designed as an operational assistant: it has a goal, context, sources, limits and an expected output. Without this framing, AI remains an interesting demo that is hard to industrialize.

  1. Signal

    Jira ticket, JSM request, published page, incident or user question.

  2. Context

    Authorized sources, Confluence spaces, Jira projects, history and business rules.

  3. Agent

    Clear instruction, limited goal, expected output and quality criteria.

  4. Validation

    Human review, permissions, traceability and publication or action rules.

  5. Action

    Comment, summary, ticket, page, notification or usable recommendation.

Agent lifecycle

Create, test and improve a Rovo agent

An agent should not remain an isolated instruction. Its design, sources, permissions, tests, metrics and continuous improvement path should be planned.

Agent design

Define role, context, tone, expected outputs, excluded cases and the human validation rule.

Sources and permissions

Select spaces, projects, knowledge bases and connectors the agent may use, while checking rights and sensitive content.

Tools and actions

Limit what the agent may suggest or trigger: comment, summary, issue, page, notification or recommendation.

Tests and evaluation

Test on real examples, incomplete cases and edge scenarios to check accuracy, robustness and usefulness.

Insights and improvement

Track adoption, quality, errors, frequent questions and content to clean before expanding usage.

Business context

Your Atlassian knowledge already exists, but it must become usable

Many companies already have the raw material for good AI use cases in Jira, Confluence and JSM: project decisions, incidents, recurring requests, procedures, tickets, decisions and lessons learned.

The real topic is turning this material into reliable context. Useful AI depends as much on Jira-Confluence practices as on Rovo configuration itself.

Useful data, but scattered

Jira, Confluence, JSM, knowledge bases and project histories already contain a lot of value, but not always in a format AI can use reliably.

Processes are not homogeneous enough

An agent does not sustainably compensate for ambiguous workflows, unused fields, poorly defined statuses or unmaintained documentation.

AI governance must be clarified

The key issue is knowing which sources the agent can use, what it can propose, who validates and how results are measured.

Ovyka support

Move from AI use case to reliable rollout

Ovyka supports French and European organizations with the scoping, configuration, governance and progressive industrialization of Atlassian AI, Rovo and Jira-Confluence agents.

AI business case scoping

Identify business friction points, volumes, teams involved and Rovo use cases that can create visible value quickly.

Jira, Confluence and JSM review

Audit projects, spaces, templates, workflows, fields, automations, knowledge bases and documentation quality.

Permissions and data hygiene

Clarify access, owners, sensitive content, authorized sources and cleanup rules before expanding AI usage.

Atlassian AI/Rovo configuration

Configure available capabilities, scopes, connectors, roles and administration practices.

Jira-Confluence agent design

Describe goals, instructions, sources, limits, human validation, expected outputs and failure scenarios.

Pilot with real teams

Test use cases in a concrete business scope, measure usefulness and correct data or process issues before expansion.

Administrator and user training

Train administrators, pilot teams and contributors on the right habits: sources, limits, validation and continuous improvement.

Continuous improvement

Track adoption, answer quality, exceptions, content to clean and new business cases to industrialize.

Rovo workshop

An entry format to move from curiosity to pilot

Before expanding Rovo broadly, Ovyka can frame a short actionable scope: sources, candidate agents, governance rules, first tests and roadmap.

Use case diagnostic

Identify visible friction points, teams involved, available sources, data risks and the first realistic Rovo scenarios.

Rovo pilot design

Select a short scope: Rovo Search, Rovo Chat, Rovo Studio, Jira-Confluence agent, connectors and validation rules.

Industrialization roadmap

Define knowledge cleanup, training, metrics, administration rules and the next candidate agents.

Scoping

Key points to frame before the pilot

These points should be clarified before broad rollout because they shape budget, security, adoption and maintainability.

What data can AI use?

Spaces, projects, knowledge bases and connectors must be selected with permissions and sensitive content in mind.

Who validates answers or actions?

The pilot states what the agent can suggest, what remains human and how errors are escalated.

How do we prove value?

Metrics should be simple: time saved, better knowledge reuse, fewer back-and-forths or higher triage quality.

What budget should we plan?

Scoping connects Atlassian licensing, configuration, data cleanup, training and post-pilot support.

Governance

Start with the use case, not the tool

Rovo becomes valuable when Atlassian knowledge is clean, responsibilities are explicit and results are measured on real examples.

View the official Rovo page Explore Rovo use cases
  • Start from a measurable business case visible to teams.
  • Clean spaces, projects, templates and knowledge bases before expanding usage.
  • Document owners, permissions, action limits and validation rules.
  • Test on real examples, including edge cases and incomplete content.
  • Plan training, administrator support and continuous improvement.
Team collaboration around Atlassian Rovo

Atlassian resources

Atlassian Rovo

Atlassian’s official pages describe Rovo, search, chat and agents. Ovyka helps translate these capabilities into pilots adapted to your context.

Ovyka

Connect AI, Atlassian and business processes

Ovyka AI services

Automation, agents, orchestration and governance for use cases beyond Atlassian.

View AI services

Your Atlassian partner

Scoping, licensing, Cloud migration, Jira, Confluence, JSM, support and TMA.

View Atlassian expertise

Jira Service Management

Portals, service desk, ITSM, knowledge base, SLAs and Jira automations.

View Jira Service Management

Confluence

Structure the knowledge base and documentation spaces that feed AI usage.

View Confluence

Ovyka references

See the portfolio of customers and organizations supported by Ovyka.

View references

FAQ

What is Atlassian Rovo?

Rovo is Atlassian’s AI experience that helps teams find, understand and use their work knowledge through Rovo Search, Rovo Chat, Rovo Studio and specialized agents.

What is the difference between Atlassian Intelligence and Rovo?

Atlassian Intelligence refers to AI capabilities embedded in Atlassian products. Rovo adds a more cross-product experience around search, chat, agents, Studio and connected work knowledge.

What is Rovo Studio used for?

Rovo Studio is used to create or adapt Rovo agents and automations with a clear scope: objective, instructions, sources, tools, action rules and validation. Ovyka helps frame those elements before broad rollout.

What are Rovo agents or bots useful for in Jira and Confluence?

They assist targeted tasks: summarizing context, organizing tickets, reviewing or structuring documentation, preparing support replies, generating summaries or suggesting actions in a defined workflow.

Which Rovo connectors should be enabled?

The right choice depends on the sources that are genuinely useful: Jira, Confluence, JSM, Slack, Microsoft 365, Google Drive, GitHub or other tools. Scoping must check business value, access rights, sensitive content and source quality.

Does Rovo work with Data Center?

Rovo is primarily tied to Atlassian Cloud offerings. Some connectors or hybrid paths may exist depending on products and licenses, but a Data Center organization should frame its Cloud path, security constraints and use cases before promising a Rovo rollout.

How can teams avoid unreliable Rovo agent answers?

Limit sources, clean knowledge, write precise instructions, define authorized actions, test on real cases and keep human validation for sensitive decisions.

How should a Rovo pilot prove value?

Metrics should stay concrete: reduced search time, better knowledge reuse, triage quality, fewer back-and-forths, team adoption and the number of exceptions to correct.

Do Rovo MCP Server and Rovo Dev matter for Atlassian teams?

Yes, but they should be scoped separately. Rovo Dev and MCP access can help technical teams or external AI assistants, provided permissions, repositories, spaces and usage rules are controlled.

Why work with Ovyka?

Support connects business goals, Jira-Confluence-JSM configuration, permissions, governance, licensing, pilot design, training and continuous improvement into a realistic path.

Want to identify the right Atlassian AI and Rovo use cases?

Let’s discuss your Jira, Confluence and JSM processes, operational friction points, knowledge quality and the first agents to test with your teams.

Request Atlassian AI scoping