Runtime Guardrails enforce safety and policy at the exact moment an AI agent attempts to take action.
Runtime Guardrails are a layer of control that sits between an AI agent and the real-world actions it wants to perform.
Unlike traditional guardrails that only inspect prompts or generated text, runtime guardrails evaluate the intended action before it executes. They decide whether an agent should be allowed to call a tool, access a system, modify data, or interact with external services.
In short: They protect what the agent does, not just what it says.
Every tool call, API request, or system action proposed by the agent is captured before it executes.
The action is evaluated against defined policies, context, identity, and risk level in real time.
The guardrail returns one of three outcomes: Allow, Block, or Require Human Approval.
Define exactly which tools and actions an agent is permitted to use.
Automatically require human approval for high-risk or sensitive actions.
Evaluate actions based on who initiated them, current context, and intended outcome.
Every decision is logged with reasoning for compliance and investigation.
Unknown or unauthorized actions are blocked by default rather than allowed.
Works with LangChain, CrewAI, AutoGen, custom agents, and more.
Safely deploy agents that interact with internal systems, CRMs, and databases without risking unauthorized changes.
Prevent agents from running destructive commands, pushing unapproved code, or accessing sensitive repositories.
Control actions involving payments, refunds, data exports, or system configuration changes.
Govern interactions between multiple agents to prevent unintended escalation or privilege abuse.
Whether you're building agent infrastructure or exploring this space, we'd love to hear from you.