Code governance
for AI agents
AI agents made code creation fast. Review, control, and security did not scale with it. CloudGrip brings a frontier-level code review agent and Cerberus vulnerability research into PRs, MRs, and Slack review channels.
Product
Two agents for AI-generated code
CloudGrip connects to GitHub, GitLab, and Slack, then brings code review and security vulnerability research into the review conversation where generated code already lands.
Repository rules turn product invariants, ADRs, incidents, and ownership notes into review context, so the agents can check what actually matters in your codebase.
Code Review Agent
Gives frontier-level review for agent-written code from PRs, MRs, and Slack review requests, checking quality, architecture drift, repository rules, and risky implementation choices.
Understands project conventions and ownership notes
Flags unclear fixes, broken abstractions, and missing tests
Returns concrete reasoning and a fix path humans can verify
Cerberus Security Agent
Researches exploitable vulnerabilities in generated code, especially auth, tenant isolation, unsafe writes, and data exposure.
Traces source-to-sink exploit paths in TypeScript and Node.js
Explains the violated boundary and blast radius
Turns confirmed risks into rules to stop repeat bugs
How it works
Connect, tag, decide
The MVP starts inside the review flow your team already uses. CloudGrip adds evidence without asking engineers to leave the PR, MR, or Slack channel.
01
Connect GitHub, GitLab, or Slack
Pick the repository and, when your team uses one, the Slack review channel where code review requests already happen.
02
Tag review or security
Mention the review agent in a PR, MR, or Slack thread, or trigger Cerberus when the change needs vulnerability research.
03
Get evidence where the review lives
CloudGrip returns affected files, reasoning, risk, impact, and a fix path in the PR/MR or the Slack review channel.
Sample outputs
Review and security findings engineers can verify
CloudGrip returns evidence in the language engineers already use: affected code, violated rule or boundary, impact, and the next fix.
Code Review Agent
Code review finding
apps/webapp/src/modules/billing/use-plan.ts
export function usePlan(user) {
const plan = user.organization.plan;
if (plan === 'trial') {
return enableEnterpriseActions();
}
return plan;
}Decision
Request changes
Risk
Trial accounts can reach enterprise-only actions
Scope
Billing hook, organization state, and action guards
Finding: trial plan branches into enterprise action enablement
Context: repository rule says paid plan checks belong server-side
Impact: generated UI can expose actions the API will later reject
Fix: derive capabilities from server-provided entitlements
Cerberus Security Agent
Security vulnerability finding
apps/api/src/modules/members/update-member.ts
async function updateMemberRole(input) {
const member = await members.findById(input.memberId);
await roles.update({
organizationId: input.organizationId,
memberId: member.id,
role: input.role,
});
}Decision
Fix before merge
Risk
Tenant-controlled organization id reaches admin mutation
Scope
TypeScript API route, service, and repository layer
Finding: request body organizationId is trusted by updateMemberRole()
Impact: cross-tenant privilege escalation for team settings
Evidence: controller does not derive organization scope from session
Fix: resolve organization membership server-side before the write
Trust
Trust starts with control
CloudGrip is not trying to replace engineering judgment. It gives reviewers enough context to control agent-written code without slowing every PR to a manual audit.
Permission-aware by default
Start with selected repositories and keep CloudGrip scoped to the review and scan workflow you actually want.
Designed around source code sensitivity
CloudGrip treats repository code as sensitive customer data and keeps findings attached to the change that produced them.
Humans stay in the approval path
The agents return evidence in PRs, MRs, or Slack review threads. Your team still decides what ships.
Findings are built to be checked
Each result points to affected code, violated rule or boundary, impact, and a fix path engineers can verify.
Governance controls
What buyers ask before connecting a repo or review channel.
Repository scope
Connect only the repos you choose
Review surface
PR, MR, and Slack review conversations
Decision owner
Humans approve, block, or request changes
Evidence model
Affected files, reasoning, impact, fix path
Start narrow, prove value, expand when the team trusts it.
FAQ
Questions buyers ask before connecting a repo or Slack channel
What does CloudGrip do?+
CloudGrip connects to GitHub, GitLab, and Slack, then brings two agents into code review conversations: a code review agent for frontier-level review of AI-generated code, and Cerberus for security vulnerability research.
How is the review agent different from Cerberus?+
The review agent checks quality, architecture, repository rules, maintainability, and fix paths. Cerberus only researches security vulnerabilities such as authorization bugs, tenant isolation failures, unsafe writes, data exposure, and policy-boundary mistakes.
Does CloudGrip replace human approval?+
No. CloudGrip is built to keep humans in control. It gives reviewers evidence they can use to approve, request changes, block risky code, or start deeper security review.
Can we start with one repository?+
Yes. The intended starting point is one production repository where AI agents touch auth, billing, customer data, admin workflows, or tenant-scoped resources. Start narrow, prove the signal, then expand.
What permissions does CloudGrip need?+
CloudGrip should be installed only on selected repositories and review channels. It needs enough access to read code changes and post findings in PRs, MRs, or Slack threads. The exact permissions should stay visible before a team connects anything.
Is this just another AI PR comment tool?+
No. The point is not to leave more comments. CloudGrip ties review output and security findings to repository context, violated rules or boundaries, impact, and a fix path so humans can decide whether generated code is safe to merge.
Start with one repository
Let agents move fast without making humans review blind. Connect the repo and review channel where generated code can create the most risk, then get evidence on the next review or vulnerability scan.