1. Durability: finished work stays finished
Every completed step is saved. If the machine dies mid-run, Smithers resumes from the last completed step and never re-runs work that succeeded. That’s the render → execute → persist loop: run a step, persist its output as a frame, and a crash just means reload the last frame and continue. (Deep version: How It Works.)
A run survives a crash mid-flight and picks up at the last completed step, with no re-doing finished work.
“Kick off the implement run. If my laptop sleeps, just resume it.”
2. It loops until true, not once-and-done
Smithers iterates. It runs implement → check → review until a condition is met, instead of taking one swing and handing you the result. One-shot output is where slop comes from; a loop with a real exit condition is how you get quality.“Keep implementing and re-running the tests until they all pass.”
3. Approvals: a paused run is just a row in a database
You can put a human in the loop. A run waiting for approval is paused as a row in SQLite, so it costs nothing while it waits: no process burning, no timer, no idle compute. Pause for a day or a week; approve, and it resumes from exactly where it stopped.“Plan the migration, then pause for my approval before touching the database.”
4. Time travel: rewind, fork, retry
Every step is a saved frame, so you can go backwards. Rewind to any earlier frame, fork from there, and try a different approach without losing the original run. The first attempt isn’t gone; it’s a branch you can compare against. (Details: How It Works.)
One run forks into two from a saved frame, so a failed approach becomes a branch you can retry instead of a dead end.
“Rewind that run to before the refactor and try a different approach.”
5. Any agent, any model
Smithers is not tied to one AI. Any agent, any model, any machine. A frontier model can plan while a cheaper one fans the work out: you pick the right brain for each step.
Plan with one model, implement with another, review with a third: Smithers routes each step to the model that fits it.
“Use a frontier model to plan, then fan the implementation out across cheaper models.”
6. Isolation: parallel work doesn’t collide
When Smithers runs work in parallel, each agent gets its own worktree or sandbox, so two agents editing the same repo don’t trample each other; the results merge back cleanly. That’s what makes fan-out safe: ten tickets, ten worktrees, ten agents, no shared mutable mess. Thekanban workflow does exactly this.
The three-layer stack (your agent on top, the Smithers runtime in the middle, isolated execution underneath) is what keeps parallel agents from colliding.
“Work all the open tickets at once, each in its own branch.”
The one rule above all six: you drive it through your agent
You never hand-write any of this. You describe the outcome and drive it through your agent, which renders the loop, the gate, the fan-out, the isolation. These six are the vocabulary for asking for exactly the run you want.Read next
Talk to your agent
How-to: how the conversation actually works, what to say, what comes back.
What you can do
Reference: the curated authoring workflows, archived examples, and the kinds of work you can hand off.
How It Works
Explanation (deeper): the render, execute, persist loop, frames, and resume in detail.
Why React?
Explanation (deeper): why a JSX runtime, and why time travel comes for free.