AI Guardrails - Enforce LLM Guardrails at Runtime
Guardrails that live in your prompt are suggestions. Agentshield enforces AI guardrails in the live action path, so the rules hold even when the model is talked out of them.
Direct answer
AI guardrails are rules that constrain what an AI model or agent is allowed to do or say. LLM guardrails enforced only in the system prompt can be overridden by prompt injection. Agentshield enforces guardrails at runtime in the request and action path: every input is inspected and every tool call is checked against policy, so unsafe or out-of-scope behavior is blocked at execution time rather than merely discouraged in a prompt.
Runtime, not prompt-only
Guardrails are enforced in the action path, so they cannot be argued away by a clever injection or a jailbreak the way prompt instructions can.
Input and output coverage
Inspect what the agent reads and what it is about to do, blocking unsafe inputs and unsafe actions with the same policy engine.
Tunable and observable
Start in observe-only to see what would trip, then tighten. Every guardrail decision is logged so you can prove what was enforced.
Where it is used
AI guardrails in the field.
LLM security
LLM security is not a model setting. It is a runtime control plane that inspects inputs, constrains actions, and records what happened, in front of every LLM app you ship.
Read more →AI agent governance
Governance is not a document, it is enforcement. Agentshield turns your agent policy into runtime controls and an audit trail that proves the rules were followed.
Read more →AI compliance
Compliance for agents comes down to one question: can you prove what your agents did and that policy was enforced? Agentshield gives you that evidence by default.
Read more →