Comparison
Patchrooms vs Plannotator: feedback that’s ready for your agent
Plannotator reviews an AI agent’s plan before it runs. Patchrooms reviews the artifact after it runs. Here is an honest look at where each fits and why most AI teams want both.
Open your first roomWhat Plannotator is good for
- Reviewing an AI agent’s plan or diff before it executes.
- Agent-native, pre-execution feedback on what the agent intends to do.
- Catching a bad approach before any code is written, saving a wasted generation.
Where teams outgrow it
- Plannotator reviews the plan; it does not review the artifact the agent produces.
- Once the app is running, you still need a way to comment on the live result.
- Teams want feedback both before (plan) and after (artifact) execution — Plannotator only covers the first half.
Patchrooms vs Plannotator
| Plannotator | Patchrooms | |
|---|---|---|
| Reviews | The agent’s plan, before execution | The running artifact, after execution |
| Anchoring | Plan steps and diffs | Live DOM elements with a selector |
| Capture | Plan text and diff annotations | Screenshot, selector, page context |
| Output | Annotated plan for the agent | Agent-ready Markdown with goal and constraints |
| Stage in the loop | Pre-execution | Post-execution |
When to use both
Use Plannotator to review what the agent is about to do, then use Patchrooms to review what it actually built. Together they cover both ends of the loop — pre-execution plan and post-execution artifact — so feedback is structured for the agent at every stage.
FAQ
- Is Patchrooms a Plannotator replacement?
- Not exactly — they cover different stages. Plannotator reviews the plan before execution; Patchrooms reviews the live artifact after. Many teams use both for full coverage.
- Do both tools produce agent-ready output?
- Yes. Plannotator annotates the plan for the agent before it runs; Patchrooms exports agent-ready Markdown about the running artifact after it runs.