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6/30/2026
Self improving code using the agentic evaluator workflow

Self improving code using the agentic evaluator workflow

Short summary

Author implements a three-agent loop where Claude Opus generates Python code, Claude Haiku scores it with structured feedback, and Opus refines it until reaching 9.6/10 quality. Key insight: passing full scoring history prevents regression and ensures consistency. Full code examples and model selection rationale provided.

  • •Three-agent pattern: generator (Opus 4.8) → scorer (Haiku) → refiner (Opus) in a loop until quality threshold hit
  • •History injection to scorer prevents regression; without it, previous scores are forgotten and inconsistently re-evaluated
  • •Structured REMOVE/ADD diff format makes refinement deterministic instead of vague instruction-following

Generated with AI, which can make mistakes.

#ai-agents#ai-tools#open-source
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