Guardrails for
AI Coding Agents
Enforce your engineering standards in real time as AI agents write code. The agent self-corrects before a PR is even created.
The Problem with Current Approaches
Every engineering organization is asking the same question: how do you let teams move fast with AI coding tools without losing control?
Context files and prompts
AGENTS.md, cursor rules, system prompts — token-expensive, non-deterministic. The AI may ignore, forget, or misinterpret rules. Maintaining 100+ standards as prompt context across every repo doesn't scale.
PR-level enforcement alone
Automated PR checks are the essential foundation — a massive step forward. But the agent works blind until it opens a PR. Feedback can be provided during authoring, so the agent self-corrects in real time.
How Agent Hooks Work
The agent writes code. Lunar evaluates policies in real time. The agent self-corrects — before opening a PR.
FROM python:3.12-slim
✗ Base image uses mutable tag
FROM python:3.12@sha256:a3d...
✓ Image pinned — all checks pass
The PR is clean on the first try. No failed checks, no back-and-forth.
Write Once, Enforce Everywhere
Same policies. Same evaluation engine.
Every stage of your development lifecycle.
Code Authoring
Pull Request
Deploy
- Fires on every file edit during authoring
- Agent self-corrects in real-time
- Automated checks on every pull request
- Block or report per guardrail
- Checks repo + SHA against policy results
- Blocks deploy on failure
Why This Matters
Same Policies, Everywhere
One set of policies governs the entire SDLC. No drift between what the AI is told and what PRs enforce. Lunar inserts at every stage.
Deterministic, Not Stochastic
Agent hooks run the same evaluation engine used in PR enforcement. Same input, same output, every time. Trustworthy enough to block.
In-Context Feedback
The agent receives structured feedback about what failed and why — exactly where and when it matters. It self-corrects before moving on.
Context Doesn't Scale. Guardrails Do.
Stuffing 100+ organizational rules into an AI agent's context window is expensive, mostly irrelevant per edit, and unreliable. Agent hooks invert this.
Lunar Agent Hooks
Compatible with All Major AI Coding Tools
Configuration is centralized and deployed to all developer machines via standard enterprise MDM tools — no per-repo setup, no developer action required.
Ready for Guardrails in the AI Era?
AI coding agents are here. The question isn't whether to adopt them — it's how to adopt them without losing control.
Let agents move fast. Keep standards non-negotiable.