Stopping AI Actions Before Execution
The author built a system that evaluates AI actions before they execute, to prevent undesirable outcomes. This shifts control earlier in the process compared to traditional approaches that react after the fact.
Why it matters
This approach is important for ensuring AI systems behave safely and reliably, especially in high-stakes domains where mistakes can be costly or difficult to fix.
Key Points
- 1AI systems today can take actions like sending emails, triggering workflows, and modifying infrastructure
- 2Most AI systems follow a pattern of deciding on an action and then executing it, with safeguards only in place after the fact
- 3The author's system checks if an AI action is allowed, matches policy, and is within scope before allowing it to execute
- 4This prevents bad outcomes instead of just reacting to them, making decisions more consistent, explainable, and enforceable
Details
The author found that many AI systems today don't just generate text, but can take real-world actions like calling APIs, kicking off jobs, and modifying infrastructure. However, the typical approach is to let the AI decide on an action and then execute it, with only logs, retries, or alerts in place after the fact to deal with any issues. The author felt this was backwards, as it means problems have already occurred by the time they are addressed. To address this, the author built a system that evaluates proposed AI actions before they are executed. It checks if the action is allowed, matches policy, and is within the appropriate scope. If the action is denied, it simply never happens. This shifts control earlier in the process and prevents bad outcomes instead of just reacting to them. It also makes decisions more consistent, explainable, and enforceable across systems, which is especially important for actions that are hard to undo like financial operations and infrastructure changes.
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