Developers are applying test-driven development methodology to AI agent prompt engineering for more reliable and iterative improvements.

Eval-Driven Development adapts TDD principles by creating evaluations first before writing prompts, ensuring measurable quality criteria

The approach enables iterative refinement of AI agent prompts through continuous testing and validation cycles

This methodology helps teams catch prompt failures early and maintain consistency across different AI agent deployments

The practice reduces debugging time and improves prompt performance by establishing clear success metrics upfront