AI agent guardrails should be built from failures, not upfront design

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.

RANK_REASON The item is an opinion piece discussing a methodology for developing AI agent guardrails, not a release or significant industry event.

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• AI
• Domain-Driven Design
• Eric Evans
• Glossary

• product
• other

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