Give the teams that had to go without an approved path
Teams barred from sending source to a hosted API have gone without AI assistance rather than break that. The gap is not appetite. Nothing approved did the job.
The exclusion is structural, so it does not resolve on its own
Without an approved path the outcome is not slower adoption. A team either works as it always has or works around the policy.
A configuration per developer is a policy surface nobody can see, let alone attest to.
One deployment is easier to approve than fifty configurations
Set once, applied everywhere
Which models are permitted is configuration held at the deployment, not in each developer’s setup.
Concurrency that queues
Runs are queued and isolated from one another rather than colliding on shared hardware.
One surface to attest to
Administration, routing and policy sit in a single place that a review can examine.
What runs between the request and the answer
Proposal
The change your agent proposes enters the write path as candidate zero, the baseline.
Alternatives
Several candidates are generated instead of the first answer being accepted.
Execution
Each runs in an isolated sandbox against the project's available checks and the runtime's own oracles.
Guarded write
An alternative must earn authorization to replace the baseline. What did not earn it carries no verification metadata.
Failures return to step 02 as repair input rather than starting over
Proposal, alternatives, execution, guarded write, with repair feeding back.
Where an approved path unblocks work first
Excluded business units
- The work
- Extending AI-assisted work to teams currently outside it.
- What repeats
- Each attempt restarts the same approval conversation and ends the same way.
- What changes
- One standardized shared deployment that is the same for every team on it.
Shared hardware
- The work
- More than one developer against inference capacity you already own.
- What repeats
- A single-user server does not queue, it collides, so capacity sits unused or contended.
- What changes
- Queueing and isolation between concurrent runs, with a shared cache.
Model policy in practice
- The work
- Keeping an approved model list true rather than nominal.
- What repeats
- A list that lives in a document is enforced by habit, which is to say not at all.
- What changes
- Routing and policy applied at the deployment, so every run inherits the same rules.
Other solutions
Get more out of the models you are allowed to run
Built to raise the reliability of whichever model your policy permits, rather than asking you to change it.
Read the solutionRun AI coding where the data is not allowed to leave
Every part of the system inside your jurisdiction, with no vendor-operated component in the path.
Read the solutionTalk through your deployment constraints
The repository and the docs need no form. This one scopes a deployment.