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Use case

AI artifact review with Patchrooms

AI builders ship a working artifact in minutes. The bottleneck is no longer building — it is reviewing what the AI made and getting precise feedback back into the agent. AI artifact review is that loop: generate, review in place, fix.

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The problem

When an agent like Lovable, Bolt, or Claude Code produces a preview, the feedback on it scatters across Slack threads, Notion docs, and loose screenshots. None of it is anchored to the artifact, so the next prompt carries no real context and the agent has to guess what you meant — often regenerating the wrong thing and burning an iteration. The faster the AI builds, the more this review gap costs.

The workflow

  1. AI generates the artifact

    Your builder ships a live preview or deployed page. Patchrooms is embedded with data-mode="artifact-review" and carries the artifact’s tool, goal, and constraints alongside every comment.

  2. Reviewers annotate in place

    Anyone with the link clicks an element and comments. Patchrooms captures the selector, a screenshot, and the page context automatically — no context switch to Slack, Notion, or a tracker.

  3. Triage in one room

    Every comment lands in a single dashboard with a status (new, triaged, in-progress, closed), so the team sees what is resolved and what is still open instead of re-reading chat history.

  4. The agent reads structured feedback

    Each report exports as Markdown carrying the selector, goal, and constraints. Paste it into your agent and it fixes the exact issue without a clarifying round trip.

Example

A team uses Lovable to build an onboarding flow and shares the preview URL. A PM clicks the "Continue" button and notes it should stay disabled until the form is valid; a designer clicks the progress bar and flags the spacing. Both comments are anchored to the real elements with screenshots. The PM exports the report and pastes it into the next Lovable prompt — the agent disables the button on the right condition and tightens the bar on the first try, no "which button did you mean?" detour.

Agent-ready export

Reports export as clean Markdown that includes the element selector, a screenshot reference, the artifact goal, and constraints. Paste it into your agent today. A Patchrooms MCP server (list_reports / get_report) that lets agents read reports directly is planned.

A report exports as Markdown like this:

## Report — Onboarding flow review
**Tool:** lovable   **Goal:** reduce signup friction
**Constraint:** keep the flow single-column

- **Element:** `button.onboarding-continue`
- **Comment:** Should stay disabled until the form is valid.
- **Screenshot:** blob/scr_9f2a.png
- **URL:** https://preview.lovable.app/onboarding

FAQ

What is AI artifact review?
It is the loop of reviewing an AI-generated artifact (a preview, page, or app) in place and feeding precise, structured feedback back to the agent that built it.
How is this different from normal bug reporting?
Normal bug reporting produces a ticket for a human. AI artifact review produces agent-ready context — selector, goal, and constraints — so the AI can fix the issue directly.
Which AI builders does it work with?
Any tool that serves a live preview. The embed runs on output from Lovable, Bolt, v0, Replit Agent, and others, and reports export back into Cursor, Claude Code, Codex, and similar coding agents.
Do reviewers need an account?
No. Reviewers only need the preview link. Comments land in your Patchrooms dashboard automatically.