EasyRoadmap exists because we wanted one place to plan, track, and communicate work without heavyweight process. The simplest way to make sure it stays practical is to do the obvious thing: we build EasyRoadmap using EasyRoadmap.
This post is a short behind-the-scenes tour of our workflow — and how our team uses AI agents extensively to speed up implementation, keep quality high, and reduce busywork.
What “dogfooding” means for us
Dogfooding isn\'t just using the product — it\'s using it as your daily operating system. That means our own roadmap has to survive:
- frequent small updates (status changes, quick notes, assignee swaps)
- planning cycles (what\'s next, what\'s blocked, what\'s important)
- release discipline (what shipped, what changed, what\'s breaking)
The principles we optimize for
One source of truth
Ideas, work-in-progress, and shipped outcomes live in one workspace — not scattered across docs, chats, and spreadsheets.
Fast planning, fast editing
Timeline for planning, Board for execution, List for quick mass edits. Each view is optimized for a different mode of work.
Decisions are traceable
We log meaningful changes and decisions so the roadmap doesn't become a mystery a month later.
AI agents accelerate the boring parts
Agents help us implement, refactor, update tests, and keep docs in sync — while humans keep ownership of product choices.
Our workflow: Timeline → Board → List
We treat the three views as three different work modes. Switching views is cheaper than changing your process.
Capture → clarify
New ideas become cards quickly with a short title and a crisp definition of done. If it's vague, it's not ready.
Plan on the Timeline
We place items on the timeline when they need sequencing, dependencies, or stakeholder visibility.
Execute on the Board
Statuses tell the truth about where work is. We keep columns lightweight so the board stays usable daily.
Maintain in List view
List is where we rename, re-assign, and clean up. It's our “fast editor” when the roadmap needs tidying.
Where AI agents fit (and where they don\'t)
We use AI agents across the team because they make the loop tighter: write → validate → update docs → ship. The goal isn\'t to automate product thinking — it\'s to remove friction.
The rule we follow is simple: humans own the intent, agents help execute. If a task is ambiguous, we tighten the spec first. If a task is clear, agents can move very fast.
Implementation bursts
When a task is well-specified, agents help ship it end-to-end: code changes, error fixes, and basic verification.
Safety & validation
Agents are great at tightening edge cases (input validation, consistent error responses, regression hunting).
Docs and deploy hygiene
Keeping deployment steps and API usage examples up-to-date is exactly the kind of work agents can do reliably.
Roadmap updates via API
We often add or adjust cards through the API using REST Client request files — fast, repeatable, and reviewable.
Traceability matters: ship work that can be explained later
Speed is useless if you can\'t explain what changed. We aim for a tight feedback loop and a clear paper trail:
- Roadmap cards define scope and acceptance criteria
- Activity logs capture meaningful changes and decisions
- API request files make changes repeatable and reviewable
This is especially important when AI agents are involved. The agent helps do the work, but the team still needs shared context — and future you needs receipts.
Try the workflow yourself
If you\'re curious, create a workspace and try this loop: add 5–10 items, place a couple on the timeline, move the rest through a lightweight board, and keep it clean in List view.