Keep AI-assisted delivery reviewable before it ships
Set policy layers, route approval decisions, and keep evidence visible when AI-assisted work touches controlled delivery.
Make AI-assisted delivery reviewable before it ships.
- 1
Set the policy layers
Define the rules, validators, and approval boundaries a governed run must respect.
- 2
Preview the effective rules
Operators can inspect the controls, context, and evidence expectations before work starts.
- 3
Decisions routed
When judgment is required, IAS routes the request to the right approver with the relevant evidence.
- 4
Review the evidence
The resulting run keeps the decisions, validator outcomes, and code changes together in one review path.
Scope and visibility boundaries
IAS still uses internal/shared/client spaces as the product model for visibility. On the feature surface, that belongs under governance: each run, decision, and context item carries the scope that determines who should see it.
- Internal
- Planning notes, risk review, and team-only context stay inside the internal boundary.
- Shared
- Shared work carries the status and decisions another party needs to review.
- Client
- Client-owned source material keeps its provenance while the team reviews it.
Evidence trail
Keep a durable record of decisions, validator results, and code changes from intent through review.
Policy Versioning
Track policy changes over time, compare revisions, and understand which rules shaped a governed run.
Decision Routing
Route approval requests to the right reviewer with the context needed to answer without losing momentum.
Policy layers
Enterprise, team, and repo rules combine into the effective controls operators can preview before work begins.
Ready to inspect a governed run?
Set up your workspace and run coding agents with the controls already in place.