Agentic Engineering in Practice: Atlassian AI Workshop
From Methodology to Execution: Deploying Autonomous Agents in Your SDLC
About the Seminar
AI agents are changing how software gets planned, built, reviewed and shipped, but most teams are still experimenting in isolation.
In this hands-on workshop we show how to make agentic engineering work in a real team, using the Atlassian stack your teams already know: Jira, Confluence, Rovo and Rovo Dev.
We start with a practical view of the agentic software development lifecycle, then move on to the shared knowledge layer that agents depend on, build agents and workflows ourselves, bring AI into the IDE with Rovo Dev and finish by measuring whether it is actually improving developer experience.
Who Is This Seminar For?
Product managers, developers, QA engineers, and project managers who want to move from AI experiments to repeatable agentic workflows.
Prerequisites
- Basic SDLC understanding
- Familiarity with Jira and Confluence
- No coding experience required
Key Topics
1. Agentic SDLC: The Generic Model
- Learn and understand the Agentic SDLC flow
- Learn how to implement skills in the process
- Learn about risks and hot to corporate best practices in the Agentic SDLC
2. Teamwork Graph & Rovo
- The future of the AI-native organization
- Turning enterprise knowledge into a competitive advantage
- Transforming how people and AI work together
- A practical roadmap from AI vision to business value
3. Creating Agents
- Discover how AI agents can support everyday business work
- Turn a real business need into a practical Rovo agent
- Build, test, and refine your first agent with expert guidance
- Leave with a working agent and the confidence to create more
4. Review and Build a Flow with Rovo Studio
- Transform business processes into intelligent agent workflows
- Connect AI agents, automations, and human decision-making
- Design and build a practical end-to-end flow
- Turn repetitive work into scalable business value
5. Rovo Dev
- Go from Jira work item to plan, code change and review with AI
- Speed up test creation and code review while keeping developers in control
- Apply guardrails and approvals that fit your team's standards
6. DX: Measuring Developer Experience
- Find out whether AI is really improving speed, quality and team satisfaction
- Choose the right signals and set a baseline
- Turn the data into decisions for your rollout
What you will take away
- Understanding Agents SDLC
- A clear, practical model for applying agents across the SDLC
- Working knowledge of the Teamwork Graph and how it grounds Rovo agents in your team's context
- At least one working agent and one automated flow built during the workshop
- Hands-on experience with Rovo Dev in a real development workflow
- A framework for measuring the impact of AI on developer experience and delivery