Guardrails AI
Run validators that detect or correct undesirable properties in LLM inputs and outputs.
Official product pageAvery Rulebook for Guardrails AI
Guardrails AI provides reusable validators for model inputs and outputs. Rulebook adds confirmed enterprise policy, contextual decisions and an evidence chain that is independent of model text.
The operating reality
The runtime compliance gate
Rulebook evaluates live context against the confirmed policy and regulatory rules that apply, then returns a decision the agent must follow.
Actor, purpose, data, jurisdiction and proposed action
Allow, deny, obligations or named human review
Evidence, rulebook version and signed decision trace
Architecture boundary
Run validators that detect or correct undesirable properties in LLM inputs and outputs.
Official product pageEvaluate whether a conclusion or action is permitted under the applicable enterprise rule set.
Guardrails AI validates content properties. Rulebook validates enterprise policy conditions and returns a governed decision.
Why the gap persists
Generic validators do not know internal authority matrices.
Validation results may not bind to a published policy version.
Corrected text can still imply an unauthorized action.
Rulebook in the workflow
Guardrails AI checks structure, safety and quality constraints.
Rulebook evaluates business context against confirmed policy.
The application returns or acts only after both control classes pass.
Potential outcomes
Questions leaders ask
The integration can compose both services. Rulebook records its own model and decision evidence where configured.
Tier 0 is deterministic. Higher tiers can use approved model-assisted workflows with that usage recorded.
Executive briefing
Bring one consequential agent workflow. We will map the governing policies and regulations, runtime gate, human authority and auditable evidence path with your team.
Request a briefing