Your AI Writes at Machine Speed.
Your Guardrails Don't.
AI agents ship code faster than any team can manually review.
Lunar's deterministic guardrails run in the agent's authoring loop, on every PR, and at every deploy gate, so your bar holds at AI speed.
AI Velocity Meets Its Next Bottleneck
AI multiplied code output. Human review bandwidth didn't. The gap compounds weekly.
Of orgs have AI governance with enforcement
41% rely on informal guidelines. 27% have nothing at all. Cortex 2026
Of leaders have data on AI code quality
91% see velocity gains from AI. Only one in four can quantify them. Cortex 2026
Writing code is no longer the bottleneck. Verifying it is.
The org-level multiplier is a deterministic enforcement layer that turns AI velocity into shipped quality, not bigger review queues.
Fast roads are engineered for safety.
So is a fast SDLC.
Same Guardrails, Three Enforcement Points.
One Engine.
Lunar evaluates policies in the AI's authoring loop, on every PR, and at every deploy gate. Same engine, same standards, every stage.
In the Agent's Authoring Loop
Lunar evaluates policies on every file edit. The agent receives structured feedback about what failed and why, and self-corrects before a PR is ever created. The human reviewer never sees the violation.
Same Guardrails for Human and AI Code
No separate governance track. The same 100+ guardrails that check human-authored PRs check AI-generated code at every stage. Your standards apply regardless of who or what wrote the code.
Deterministic, Not Stochastic
AI reviewers are a great complement for the judgment calls: the things that aren't yet encoded as policy. Lunar handles the must-pass standards, with the same input producing the same output every time, trustworthy enough to actually block.
Three Enforcement Points, One Engine
Agent hooks during authoring, PR checks before merge, deploy gates before production. Same policies applied at every stage. No drift between what the agent saw and what production enforces.
Zero Context Overhead
AGENTS.md and cursor rules crowd the context window, and long context degrades retrieval. Lunar runs externally, evaluating only the rules relevant to the file being edited. Your standards can grow without shrinking the agent's working space.
Visibility Into AI Code Quality
Answer "is AI code meeting our standards?" with data, not hope. Track guardrail pass rates, time-to-merge, standards violations. The metrics the board actually asked about.
The Engineering Bar, Enforced at AI Speed
Deterministic checks that run in the agent's authoring loop and on every PR. AI ships at its natural speed. Your engineering bar holds.
:latest tags. Require approved base images. Keep the supply
chain pinned regardless of whether a human or an agent picked the image.
Why Platform Engineering Leaders
Are Building AI Governance Now
agent hooks, PR, deploy gate
(Lunar runs externally)
human and AI code alike
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 100+ built-in guardrails.
AI agent rules & prompt files
Post-mortem action items
Security & compliance policies
Testing & quality requirements