The best time to stop an AI agent run is often before the damage is obvious.
That sounds strict, but shared state is where small mistakes become expensive. A run that changes a local file in one branch is easy to inspect. A run that touches a shared branch, a migration, a package lockfile, or a release path has a larger blast radius. The later you notice the drift, the more cleanup you buy.
This is the rule to keep in mind: stop before shared state damage.
What counts as shared state
Shared state is anything other people, builds, or deployments rely on.
In a typical repo, that includes:
- shared branches
- migrations
- lockfiles
- release scripts
- CI configuration
- environment templates
- anything that affects production or a team-wide workflow
That list is broader than "production code." A harmless-looking config change can still create a team problem if it affects everyone.
Junction's local-first model helps here because the agent execution stays on the user's machine. But local execution does not remove blast radius. It only makes the control surface clearer while the work is still in flight.
Why stopping early is the right move
People often wait too long because they want the run to finish.
That is understandable. A run that is already 80 percent done feels close to done. But shared state turns the last 20 percent into the risky part. Once a migration, release file, or shared branch change is on the table, you are no longer just finishing a task. You are deciding whether the task is still the task you wanted.
Stopping early is cheaper than trying to explain a mixed-intent diff later.
That is especially true on mobile. If you are reviewing from your phone, you want an alert that tells you when the run crossed a boundary, not after the branch already has several unrelated edits.
Signals that justify a stop
There are a few useful triggers:
The task started to drift
The agent is doing work that was not in the prompt, or it has begun solving adjacent problems without asking.
The diff crossed from isolated to shared
The run started in a small, reviewable area and then moved into files that multiple people depend on.
The command changes something outside the repo
If the run wants to touch a database, a service, or another external system, the approval threshold should be higher.
The output no longer matches the risk level
Sometimes the content of the diff looks small, but the underlying change is important. A migration or branch rename can be more consequential than the line count suggests.
The run needs a different owner
If a task now depends on product judgment, infrastructure judgment, or release coordination, it may need a person to take over.
A concrete example
Imagine an agent fixing a bug in a local service.
It starts well:
- read the failing test
- patch the implementation
- update a focused assertion
Then it asks to help with the migration that the test uncovered.
That is the moment to stop and inspect the boundary.
Maybe the migration is legitimate. Maybe it is the right next step. But the important thing is that the work class changed. The original run has reached shared state, and you should decide deliberately whether to continue in the same thread or split the migration into a new task.
The same logic applies to a run that starts touching a shared release branch or a company-wide config file. Once the work stops being private to one isolated task, the review standard needs to change.
How Junction makes the stop easier
Junction is designed for this exact moment.
You can watch the live output, see the current diff, and stop the run from the same control surface. That matters because the decision should happen while the agent is still active, not after you have lost track of where the change came from.
The built-in code review view is also useful because it lets you separate "fine so far" from "this changed category." That distinction is easy to miss if you only have a terminal transcript.
What to do after you stop
Stopping a run is not the end of the workflow. It is the handoff point.
After the stop:
- inspect the current diff
- decide whether the shared-state change belongs in the same task
- split the work if the answer is no
- restart with a narrower prompt if needed
That sequence keeps the next run honest. The agent resumes with a cleaner boundary and a clearer objective.
When not to stop
Not every boundary needs a hard stop.
If the task is still isolated, the diff is easy to review, and the work remains inside the original scope, continue. You are not trying to interrupt every meaningful edit. You are trying to catch the moment when the edit stops being local enough to trust casually.
That is an important distinction. A good stop rule does not make the workflow timid. It makes it selective.
The benefit over time
Teams that use this rule tend to build better habits around agent supervision.
They start treating shared state as a deliberate event instead of an accidental side effect. That lowers cleanup time, makes approvals less noisy, and keeps the review surface smaller.
It also makes it easier to trust local agents over long periods because the operator learns to catch the boundary early instead of after the run has already spread.
If you want that boundary visible in Junction, start with the setup guide. If you are deciding how much control surface you need, compare pricing and then read How to Approve AI Agent Actions Safely Without Slowing Down.