The framework helps to formulate tasks in a way that avoids misunderstandings between the "client" and the "executor":
- Helps to clearly articulate the goal, context, and criteria for task completion.
- Makes the task transparent and understandable for executors and clients.
- Suitable for tasks with predictable cycles and measurable results.
- Does not replace flexible processes in research, innovation, or dynamic projects.
Structure:
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G — Goal: Why are we performing the task? What is the business goal?
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O — Overview: What is happening now? Why did the task arise? What is happening around it?
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D — Definition of Done: When do we consider the task completed? By what criteria?
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S — Solution: How can the task be solved?
The framework is applicable where:
- The goal of the task is clear.
- The context is clearly described.
- There are measurable criteria for completion.
- The executor is free to choose the solution.
Ideal for:
- Incidents.
- Operational DevOps tasks.
- Infrastructure development tasks.
- Requests from product teams.
- Business processes with clear outcomes.
- Tool implementation.
- Testing, change management, product launches.
Ineffective for:
- Innovative projects without clear outcomes.
- Tasks with rapidly changing requirements.
- Processes with a high degree of uncertainty, where flexible management is needed.
- Projects where it is important to control intermediate stages, risks, and strategy adaptation.
- Goal: Restore the CI/CD pipeline so that deployment delays do not exceed 1 hour.
- Overview: Updated the Jenkins plugin → 5 builds with errors → version incompatible.
- Definition of Done: All builds pass without errors within a day, tests ≥98% successful, team notified.
- Solution: Roll back the plugin, patch, or change the tool after log analysis.
- Goal: Update the monitoring system to reduce false positives.
- Overview: Outdated rules → too many alerts.
- Definition of Done: False positives ↓80%, checks passed, documentation updated.
- Solution: Use a ready-made runbook and scripts.
- Goal: Implement a predictive analytics module to prevent incidents.
- Overview: Incident frequency has increased by 25%, no forecasting tools available.
- Definition of Done: Module integrated, accuracy ≥85%, pilot tested within 3 months.
- Solution: Research ML models, selection after analysis.
- Goal: Move the Alpha service to Docker to speed up deployment and simplify support.
- Overview: Currently, Alpha runs on virtual machines, deployment takes a long time and causes errors during updates.
- Definition of Done:
- The service runs in Docker.
- Deployment time reduced by 50%.
- All tests pass successfully.
- Documentation updated.
- Pilot launch confirmed.
- Solution: Use Docker Compose, CI/CD integration, and monitoring setup.