Strong fit
- Multi-file changes with explicit requirements.
- High-impact work with repeatable validation commands.
- An independent pass after normal checks have run.
Use case · AI code review
AI code review that checks the project, not just the diff
Pair GPT-6 Astra with a compatible coding Agent inside a persistent Workspace. Inspect the change in project context, connect findings to evidence, run available checks, and leave a review packet a person can verify.
- Scoped diff
- Evidence-backed findings
- Human acceptance
01 · Working evidence
A useful AI review ends in evidence
Every finding should name the failing behavior, its impact, the supporting code or check, and the smallest way a reviewer can verify it.01src/auth.tsScoped diff
02P1 · behaviorEvidence-backed findings
03bun testHuman acceptance
Open the project, choose a compatible Agent and GPT-6 Astra in the live selector, and begin with one bounded change.
Review a project02 · Workflow
Turn one bounded change into a review packet
The model proposes findings. The Agent harness supplies project access, tools, permissions, and the verification loop.- 01
Define the review boundary
Choose a diff, expected behavior, protected constraints, and the commands that count as acceptance checks.
- 02
Inspect and rank evidence
Follow only the call sites, tests, and configuration needed to understand the change, then prioritize behavior over style.
- 03
Verify before patching
Run the narrowest relevant checks, separate confirmed findings from hypotheses, and let a person approve any fix.
03 · Task fit
Use AI review where context and evidence matter
A second review pass can reduce missed connections, but it does not replace accountable approval.Not the right fit
- A repository with no clear diff or intended behavior.
- Formatting already enforced by deterministic tools.
- Automatic approval without a responsible reviewer.
04 · Boundaries
Treat every finding as a claim to verify
GPT-6 Astra can miss defects or produce false positives. Project checks and human review remain part of the result.- 01
A large context window does not prove the right evidence was retrieved.
- 02
Model-level tools are not automatically exposed by every Agent harness.
- 03
Passing tests do not prove the absence of security or logic problems.
06 · Questions
Before you ask an Agent to review code
Keep the scope, execution layer, and approval boundary explicit.
Start in one Space
Turn one diff into a review you can verify
Open the project, choose a compatible Agent and GPT-6 Astra in the live selector, and begin with one bounded change.