assess-hydrator

To evaluate prompt-hydrator agent quality, analyze real session logs to assess intent accuracy, workflow selection, context relevance, and generated plan quality. The process extracts agent interactions and identifies anti-patterns to prevent errors in downstream AI workflows.

Agent evaluation in prompt engineering workflows is the systematic assessment of AI agent performance using real interaction data. It identifies issues like intent inaccuracy and poor context relevance to ensure generated plans meet quality standards before impacting downstream agents.

Track quality assurance trends by storing agent evaluation findings in a dedicated data directory after each assessment. This accumulates evidence from session analysis, allowing you to monitor hydrator quality and identify recurring areas for improvement.

Yes, you can identify workflow selection errors by analyzing prompt-hydrator session logs. The assessment process extracts real agent interactions and flags poor workflow choices, irrelevant context, and anti-patterns in generated plans.

During session analysis of prompt-hydrators, look for intent inaccuracy, poor workflow selection, irrelevant context generation, and anti-patterns in plans. These issues directly impact the accuracy and relevance of context provided to downstream agents.