Gates Earned From Failure: a cost test for agent guardrails
The author advocates for building guardrails for AI agents based on real-world failures rather than upfront design. They describe a process where a bug, such as a glossary drifting from the actual domain model, signals the need for a specific check. This approach emphasizes learning from mistakes to create targeted enforcement mechanisms, like pre-commit hooks, which are more effective than relying on manual discipline or extensive code reviews. AI
IMPACT This approach suggests a more iterative and failure-driven method for developing robust AI agent systems.