AI-Based Process Creation
Use AI to generate release processes from natural language descriptions.
AI-based process creation allows you to generate release processes by describing them in natural language. The AI analyzes your description and creates a structured process with phases, activities, and dependencies.
How it works
Harness supports creation of processes using Harness AI, which is an AI-based approach. Most of the time, release processes are available in a textual fashion documented in different sources. Release Orchestration enables users to provide that process documentation and create the process as an entity in Harness Release Orchestration.
Provide process documentation
Provide a natural language description or documentation of your release process. Use one of the following:
Textual documentation from existing sources
Process descriptions from organizational documentation
Multi-service release processes with planning, building, validation, deployment, and monitoring phases
Example:
"Multi-service release process starting from planning of the release up until building
and doing other functions, different heterogeneous functions under one umbrella, using
a process up until releasing and monitoring in production. The process includes:
- Release planning and coordination (Owner: Release Manager)
- Build and artifact creation
- Testing and validation
- Feature flag enablement
- Production deployment
- Monitoring and rollback"Analyze and generate the process
Once you provide the prompt and ask the AI agent to create the process, it automatically:
View the process
The process is visualized in a graphical view, showing:
All phases that have been created (from release planning and coordination up until rollback and documentation)
Activities within each phase
Dependencies between phases and activities
Owner assignments
Review and save
After AI generation, you can:
Review the generated phases and activities
Verify owner assignments
See a summary of what the process is enabling (modeling the entire process as an entity and enabling orchestration using activities)
Save the process
Best practices for AI process creation
Be Specific
Provide detailed descriptions:
Recommended: "Deploy to staging, run smoke tests, wait for QA approval"
Avoid: "Deploy and test"
Include dependencies
Mention what must happen before other steps:
Recommended: "After deployment completes, run integration tests"
Avoid: "Deploy and test"
Specify activity types
Indicate what should be automated vs manual:
Recommended: "Automatically run unit tests, manually review security scan results"
Avoid: "Run tests and review"
Include approval points
Mention where approvals are needed:
Recommended: "Require production deployment approval from release manager"
Avoid: "Deploy to production"
Refine AI-generated processes
After the AI generates a process:
Review the structure: Ensure phases and activities make sense
Check dependencies: Verify execution order is correct
Validate activities: Confirm activity types and configurations
Add details: Enhance with specific configurations
Save: Save the process and start adding reusable activities
Example workflow
Input description
AI-generated process
The AI creates:
Phase 1: Preparation
Code freeze activity
Branch creation activity
Phase 2: Build
Build Service A (automated)
Build Service B (automated)
Build Service C (automated)
Phase 3: Integration Testing
Deploy to integration (automated)
Run integration tests (automated)
Phase 4: Staging
Deploy to staging (automated)
UAT (manual)
UAT sign-off (approval)
Phase 5: Production
Production approval (approval)
Deploy to production (automated)
Post-deployment validation (automated)
AI process limitations
AI-generated processes are a starting point. Consider the following:
May require refinement for complex scenarios
May not capture all organizational nuances
Should be reviewed by subject matter experts
May need customization for specific tools and integrations
Related Topics
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