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AI-Based Process Creation

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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.

1. Provide Process Documentation​

Provide a natural language description or documentation of your release process. This can be:

  • 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"

2. AI Analysis and Generation​

Once you provide the prompt and ask the AI agent to create the process, it automatically:

3. Process Visualization​

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

4. 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"

Refining AI-Generated Processes​

After the AI generates a process:

  1. Review the structure: Ensure phases and activities make sense
  2. Check dependencies: Verify execution order is correct
  3. Validate activities: Confirm activity types and configurations
  4. Add details: Enhance with specific configurations
  5. Save: Save the process and start adding reusable activities

Example Workflow​

Input Description​

"Multi-service release process:
1. Code freeze and branch creation
2. Build all services in parallel
3. Deploy to integration environment
4. Run integration tests
5. Deploy to staging
6. User acceptance testing with sign-off
7. Production deployment with approval
8. Post-deployment validation"

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)

Limitations​

AI-generated processes are a starting point:

  • 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