Implement Agentic Workflow for Localization - https://github.com/RevealBi/Documentation
https://github.com/github/gh-aw/blob/main/AGENTS.md
Notes:
The solution **already “kind of works” but is not stable, fails intermittently and still feels like a beta system with changing implementation.
How Agentic Workflow Works?
- You don’t manually implement logic-heavy pipelines. Instead:
- You describe your intent in natural language
- Use a predefined “skill” (agent capability)
- The system generates the workflow for you
In a nutshell you tell the model what you want, and it builds the workflow job for you.
Example Workflow Behavior (Core Use Case)
The workflow should:
- Monitor a folder (English docs)
- Detect changes
- Translate content into Japanese
- Create a PR with the translated files in a target folder .. i18n/ja/docusaurus-plugin-content-docs/
Structure should be:
- English docs → source
- Japanese docs → generated output
- PR → synchronization mechanism
- Docs Handling Concept There is a distinction:
- Source docs (editable)
- Generated files (compiled / not editable)
Notes:
- Generated outputs (e.g. compiled YAML or output files) → must NOT be edited manually
- Only modify the source (e.g. markdown), then recompile/generate
Key Takeaways
- Agentic workflows:
- Are prompt-driven, not pipeline-heavy
- Build automation from intent, not scripts
- Localization use case is a perfect fit
- Docs pipeline must:
- Keep source → generated separation
- Automate PR generation for translations
- Expect:
- Instability (beta behavior)
- Iteration needed
Implement Agentic Workflow for Localization - https://github.com/RevealBi/Documentation
https://github.com/github/gh-aw/blob/main/AGENTS.md
Notes:
The solution **already “kind of works” but is not stable, fails intermittently and still feels like a beta system with changing implementation.
How Agentic Workflow Works?
In a nutshell you tell the model what you want, and it builds the workflow job for you.
Example Workflow Behavior (Core Use Case)
The workflow should:
Structure should be:
Notes:
Key Takeaways