Separating generation and evaluation agents prevents overconfident self-grading¶
Insight: Using distinct agents for generation and evaluation prevents agents from confidently praising mediocre work. Teams employing multi-agent architectures (planner, generator, evaluator) benefit from clean context resets between sessions while maintaining continuity through structured handoffs. Breaking complex work into explicit feature contracts with success criteria improves coherence.
Detail: For subjective domains, establishing concrete evaluation criteria transforms vague judgments into measurable standards. The approach requires regular iteration to question which harness components remain necessary as models improve.