Active guardrails

How preset AI moves from passive guidance to active constraint enforcement for AI workflows.

Objective

Ensure AI-generated output is system-aligned by design, not corrected after drift appears.

Guardrail model

Active guardrails combine three moments:

• Constraints before generation: the agent reads what's allowed before writing
• Validation after generation: generated code is checked against rules
• Policy gates before apply: canonical writes are gated on approval

Together these create a controlled loop instead of ad-hoc assistant behavior.

Core guardrail layers

1. Forbidden primitives and anti-pattern constraints
2. Context-aware active constraints by file, intent, or surface
3. Pre-generation validation checks
4. Post-generation code validation and drift checks
5. Mode-aware apply policy gates

Expected outcomes

• lower cleanup work after AI generation
• fewer policy violations entering PRs
• more predictable assistant behavior across teams

Integration guidance

Use guardrails as the default policy in AI-assisted coding flows:

• retrieve constraints first
• generate within constraints
• validate before commit or apply
• route unresolved cases into the proposal workflow

Relationship to compliance modes

Each compliance mode tunes how strictly guardrails block versus advise:

• Observe / Assist: emphasis on guidance and learning
• Guard / Enforce: stricter blocking and approval requirements

See Rule model for the underlying rule contract and Validation and confidence for how guardrail results feed scoring.