ChartaFlow
From product knowledge to published documentation.
A documentation platform I’m building with AI coding agents, bringing together authoring, team permissions, publishing, and AI-assisted discovery.
The problem and my contribution
Support teams need documentation they can maintain confidently and customers can find when something goes wrong. My experience supporting enterprise platforms shapes ChartaFlow’s focus on usable authoring, reliable publishing, and clear access boundaries.
I define requirements, scope agent tasks, review changes, investigate regressions, and verify releases. AI agents help implement the product; I guide the work and evaluate whether the result meets the intended behavior.
What works in the current codebase
- Authoring: rich-text editing, Markdown import, draft recovery, and edit history.
- Publishing: separate draft and public content, organized article navigation, and search.
- Collaboration: project invitations, roles, and permissions enforced beyond the interface.
- AI assistance: writing support and a documentation assistant that retrieves from project knowledge.
- AuthorTipTap editor
- Save draftPrivate editable state
- PublishExplicit permission check
- ReadPublic documentation
Editing a published article must not change its public version until publication.
A problem I worked through
The editor and navigation tree can both change an article’s metadata. A publishing regression showed why those surfaces need a shared contract: stale editor state could overwrite a more recent rename, move, or ordering change.
The project now shares page-persistence logic and has a browser regression that exercises those changes before publishing. Related checks cover draft isolation and restricted authors. This turns a specific failure into repeatable evidence for future changes.
How I plan and verify changes
I start with the user-visible outcome, acceptance criteria, and a reproduction path for bugs. I map affected areas, security constraints, dependencies, and agent ownership before implementation, then define the checks that will demonstrate completion.
Verification combines TypeScript and unit tests, a disposable Supabase harness for real database policies, and Playwright browser flows. CI runs the full suite. Scoped checks support iteration; deployment is followed by checking the actual affected route.
Skills developed through the work
Agentic development: task design, specialized agent roles, model and reasoning choices, and independent review.
Engineering: React and TypeScript, editor state, API contracts, Postgres permissions, and debugging across frontend and backend boundaries.
Delivery: Git branches and worktrees, scoped changes, regression tests, migrations, and Vercel releases.
ChartaFlow remains in development. The project gives me practical experience connecting customer needs to implementation and testing across a full application.
Read about my agent workflow ↗