create-agent

Automating quality assurance for workflow steps involves generating validation agents that score outputs across 5 dimensions, provide actionable rewrites, and enforce hard violations. Python is required to run the agent generation and analysis scripts.

Hard violations in workflow validation agents are critical errors that trigger an instant fail, meaning the output is rejected regardless of how well it scores on other quality dimensions. They enforce strict compliance gates that cannot be compensated by other measures.

Yes, you can define customizable scoring dimensions to evaluate quality based on the specific requirements of your workflow step. You can also provide calibration examples to standardize the scoring across these 5 dimensions.

Yes, Python is a required dependency for generating validation agents and running the analysis. You need a Python environment configured to execute the scripts that create and evaluate the validation logic.

The best way to standardize quality scoring is to provide calibration examples when generating the validation agent. These concrete examples standardize the scoring behavior across the 5 defined dimensions for consistent workflow validation.

You should set instant fails for critical errors when a mistake cannot be compensated by other quality measures. Configuring hard violations ensures that outputs with severe compliance issues are immediately rejected by the validation agent.