Engineering Guardrails for the AI Era.
Turn
eng wikis, AI prompts, standards, AGENTS.md, infra rules, checklists, and
compliance
into deterministic PR and AI-level enforcement
in minutes,
not quarters
Backed by
Developer behavior
is difficult to change at scale.
Standards Are Scattered
AGENTS.md, wikis, Slack threads nobody follows
Checklists Get Rubber-Stamped
By humans and AI alike — no way to verify compliance
Human Review Is the Bottleneck
It's hard to keep up with AI-generated code volume
Fragmented Tooling
Every repo configured differently, no central enforcement
Issues Surface Too Late
Non-compliant code caught at deploy time, not authoring time
❌ The Broadcast Problem
Standards communicated
out-of-context
through mass channels
Any Guardrail, One Prompt Away
Describe any standard, process, or incident response. Lunar turns it into an enforced guardrail across every repo, in minutes.
claude
❯
✻ Thinking
Creating collector and policy…
collector.sh
#!/bin/bash
for
done
policy.py
from
check
for
auth-svc
frontend
api-gateway
payments
search
cdn-proxy
k8s-infra
user-svc
notify
billing
analytics
inventory
admin
ci-runner
data-pipe
ml-engine
docs
mobile-api
scheduler
config-svc
cache
queue
logging
terraform
15% compliant
PR #423
PR #421
PR #419
PR #418
PR #415
-
1
Describe
Postmortem finding, compliance mandate, or zero-day. In plain language.
-
2
AI Creates the Guardrail
Data collection and enforcement. The platform handles both.
-
3
Enforced Everywhere
Every PR, every repo. Minutes, not quarters.
AI Generates Code Fast.
Guardrails Ensure It Ships Right.
Deterministic enforcement for developers and AI agents. Centrally instrumented, gradually rolled out, audit-ready by default.
Deterministic,
Not Stochastic
Guardrails run as code, not prompts. Same input, same output, every time. Trustworthy enough to actually block a PR or a deploy.
Same Guardrails,
Human and AI
One set of standards, uniformly enforced. No separate AI governance track. Works with Claude Code, Cursor, Codex, and every pull request.
Central
Instrumentation
Deploy once, cover every repo and pipeline. No per-team opt-in, no template drift, no repo-by-repo rollout.
Evidence as
a Byproduct
Real-time adherence dashboards and a continuous audit trail fall out of enforcement. Not a separate quarterly exercise.
Gradual
Enforcement
Start with visibility, add PR comments, escalate to blocking. Adjust centrally, without repo-by-repo opt-in.
Works With
Your Stack
GitHub, GitLab, any CI/CD. Complements OPA, Rego, and existing policy tools with the structured SDLC data they need.
Write Once, Enforce Everywhere
Same policies. Same evaluation engine.
Every stage of your development lifecycle.
Code Authoring
Pull Request
Deploy
Developer / AI Production
Agent Hooks
- Fires on every file edit during authoring
- Agent self-corrects in real-time
PR Checks
- Automated checks on every pull request
- Block or report per guardrail
Deploy Gates
- Checks repo + SHA against policy results
- Blocks deploy on failure
200+ Guardrails Included
Enforce standards across reliability, quality, security, and compliance — for human-written and AI-generated code alike.
CodeCov required
Enforce code coverage tool usage in CI
Prevent unpinned base images
Block PRs that introduce :latest tags in Dockerfiles
K8s resource limits required
Verify CPU and memory requests are defined
SBOM generation in CI/CD
Meet NIST SSDF requirements
Security vendor coverage
Detect Snyk/Trivy usage across all production repos
Valid CODEOWNERS file
Ensure every service has clear ownership
Repository management • Build & CI • Deployment • Security • Compliance • Operational readiness
Ready to Automate Your Standards?
See how Lunar can turn your AGENTS.md, engineering wiki, compliance docs, or postmortem action items into automated guardrails with our 200+ built-in guardrails.
Works with any process
AI agent rules & prompt files
Post-mortem action items
Security & compliance policies
Testing & quality requirements