Runtime Guardrails for AI Systems
Runtime guardrails operate at the moment an AI system attempts to execute an action. They evaluate the action against policies and determine whether it should be allowed, blocked, or require human approval.
Runtime Guardrails are part of the broader category of AI Guardrails that control what AI systems are allowed to do when interacting with production systems.
A Runtime Control Plane for AI Actions is the infrastructure layer that sits between AI systems and real-world tools, deciding whether actions should be allowed, blocked, or require approval before execution.
How Runtime Guardrails Work
Runtime guardrails intercept every action an AI system attempts to execute. Before the action reaches any external system, the guardrail evaluates it against predefined policies and makes a decision:
The action passes policy checks and executes immediately.
The action violates policies and is prevented from executing.
The action requires human review before it can proceed.
This three-outcome model gives organizations fine-grained control over AI behavior while maintaining operational efficiency for low-risk actions.
Runtime Guardrail Architecture
Runtime guardrails sit at the execution boundary between AI systems and production infrastructure. This positioning ensures that every action is evaluated regardless of how the AI system generated it.
AI System
Generates action requests
Runtime Guardrails
Evaluate → Decide → Allow/Block/Approve
Production Systems
APIs, Databases, Services
Systems Protected by Runtime Guardrails
Runtime guardrails can protect any system that AI agents interact with. Common protected systems include:
APIs
Control which external APIs AI systems can call and what operations they can perform.
Payment Systems
Require approval for financial transactions above thresholds. Block unauthorized payment operations.
Infrastructure
Prevent AI systems from making unauthorized infrastructure changes or deployments.
Databases
Control database operations. Block destructive queries. Require approval for bulk modifications.
Messaging Systems
Control email, SMS, and notification systems. Prevent mass messaging without approval.
Example Runtime Guardrail Policies
Runtime guardrails are configured through policies that define what actions are allowed, blocked, or require approval. Here are common policy patterns:
# Block all DELETE operations on production databases
action: database.delete
environment: production
decision: BLOCK
# Require approval for payments over $1000
action: payment.transfer
condition: amount > 1000
decision: REQUIRE_APPROVAL
# Allow read operations from trusted APIs
action: api.get
target: internal-api.company.com
decision: ALLOW
How Runplane Implements Runtime Guardrails
Runplane provides runtime guardrails through its control plane architecture. AI systems integrate with Runplane through native integrations for popular frameworks like LangChain, CrewAI, and custom implementations.
Every action request is sent to Runplane's policy engine, which evaluates the action against configured rules and returns a decision. The entire evaluation happens in milliseconds, adding minimal latency to AI operations.
For actions requiring approval, Runplane queues the request and notifies designated approvers through Slack, email, or the Runplane dashboard. Once approved, the action executes automatically.
Related Concepts
AI Guardrails
The different layers of guardrails for AI systems.
AI Runtime Governance
Systems and policies that evaluate AI actions before execution.
Execution Containment
Intercepting high-impact AI actions before they execute.
Runtime Policy Engine
How policies evaluate and approve AI actions.
AI Blast Radius
Limiting the potential impact of AI actions.
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