How do runtime guardrails protect enterprise LLM applications and AI agents?

Runtime guardrails are policy enforcement that runs during live AI interactions - before any action actually executes.

They can block malicious prompts, restrict which tools an agent can call, prevent unauthorized resource access, stop sensitive data from being exposed, enforce compliance policies, and filter harmful content.

The key difference from static controls is timing. Guardrails evaluate every interaction as it happens. By the time a guardrail fires, nothing has executed yet.

Akto's MCP and AI Agent Guardrails let organizations define custom policies governing prompts, tools, resources, and outputs. Security teams can specify exactly what agents are and aren't allowed to do, and those rules are enforced at runtime without requiring manual review of every interaction.

The goal isn't to make AI agents useless through over-restriction - it's to keep them operating within approved boundaries while still delivering business value.