AI Agent Workflows Need Better Tests

The strongest agent strategy is disciplined workflow selection, not maximal autonomy. AI adds value when it operates inside a defined control loop with observable success criteria.

• –Repeated triggers create enough volume to justify automation and generate evaluation data.
• –Stable inputs reduce ambiguity, while clear tool contracts limit agent drift.
• –Measurable finish lines turn vague “agent quality” into pass/fail outcomes and business metrics.
• –Human checkpoints remain useful for exceptions and high-impact decisions.
• –The framework aligns with the industry shift toward dependable AI workflow automation rather than open-ended assistants. [Product Hunt’s workflow automation analysis](https://www.producthunt.com/categories/ai-workflow-automation)