The Problem with Reactive AI Safety
Imagine you're designing security for a bank vault. Would you put the security system after people have already entered the vault and taken the money? Of course not. You'd require authorization before they can enter.
Yet this is exactly how most AI safety systems work today. They operate reactively—filtering outputs after AI models have already made decisions, rather than governing actions before they're executed.
Guardrails: The Current Approach
Most AI safety implementations today follow the guardrails model:
AI Model → Output Generation → Safety Filter → Approved Output
This works well for content-focused applications, but has critical weaknesses when applied to autonomous agents:
Timing Problems
Guardrails operate after the AI has already decided what to do. For autonomous agents, this is often too late.
Execution vs. Content
Guardrails filter what AI systems can say. But autonomous agents need governance over what they can do.
Execution Warrants: A Proactive Model
Instead of filtering outputs, execution warrants govern actions at the intent level:
Agent Intent → Risk Assessment → Approval → Signed Warrant → Execution
Every approved action receives a cryptographically signed warrant with:
Real-World Example
Scenario: AI agent wants to scale database cluster
Guardrails approach:
Warrant approach:
Why Warrants Work
1. Proactive control: Stop problems before they happen
2. Risk-aware: Different approval flows for different risk levels
3. Cryptographically verifiable: Tamper-evident audit trails
4. Time-limited: Warrants expire, preventing stale authorizations
5. Scope-constrained: Agents can only do exactly what's approved
Learn how to implement execution warrants in your systems. Read the docs →