Queued AI: What Happens After You Add a Task (Clarify → Queue → Execute → Review)
The interesting part of Queued AI isn't the prompt box — it's everything that happens *after* you hit add. This is a step-by-step look at the four stages every task goes through: Clarify → Queue → Execute → Review.
Stage 1 — Clarify
Most AI coding failures come from a confident agent guessing at ambiguous scope. Queued AI closes that gap up front. Right after you add a task, it asks a short set of clarifying questions — only the ones that actually change the implementation.
- →Which screen or module does this touch?
- →Should this match an existing pattern in the codebase, or introduce a new one?
- →Are there edge cases or states to handle explicitly?
You answer with a tap or a sentence. This tiny investment up front is what makes the resulting pull request land closer to what you actually meant.
Stage 2 — Queue
Once clarified, the task enters your queue. This is the async heart of Queued AI: tasks run in order, one after another, without you supervising. You can stack several tasks, reorder priorities, and then put your phone away.
Queuing decouples *deciding what to build* from *waiting for it to build*. You spend ten seconds describing the outcome; the agent spends the minutes executing it.
Stage 3 — Execute
When a task reaches the front of the queue, the agent goes to work:
- 1Indexes the architecture — it maps your repo so changes fit your existing structure, not a generic template.
- 2Writes the change — implementing the feature, fix, or refactor across the files it touches.
- 3Self-checks — it works against your project to catch obvious breakage before you ever see it.
Stage 4 — Review
The output of every task is a pull request on GitHub — never a silent push to main. You get a clean diff to read, approve, comment on, or reject. This is deliberate: the review step is where you keep control and where quality is enforced.
- →Approve and merge if it's right.
- →Request changes and requeue a follow-up if it's close.
- →Close it if the approach isn't what you wanted — no harm done.
Why this flow beats live prompting
Live prompting keeps you in the loop for every token. The Clarify → Queue → Execute → Review flow only asks for your attention at the two moments that matter: describing the goal, and approving the result. Everything in between runs without you.
Frequently asked questions
How is Queued AI different from a chatbot coding assistant?
A chatbot is synchronous — you prompt and wait. Queued AI is asynchronous — you queue a task and get back a pull request. You're only involved at the start and the end.
What if the clarifying questions aren't enough?
You can add detail to the task description at any point before it runs. More context up front generally means a tighter pull request.
Can I queue multiple tasks at once?
Yes. Tasks run in order. Add as many as you like, reprioritize, and review the pull requests as they arrive.
Does anything merge automatically?
No. Every task ends in a pull request you review. Merging is always your call.
Queue your first task
Describe a task, walk away, and wake up to a reviewed pull request. Free to download — no credit card required.