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agentic-eval

Evaluate and refine AI agent outputs through iterative feedback loops.

Updated Mar 3, 2026
Search Tags:#AI evaluation#self-critique#iterative refinement#agent improvement#evaluation strategies
One-click install
npx skills add https://github.com/arielybarak/agent-lab --skill agentic-eval-arielybarak
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-eval
Source: https://github.com/arielybarak/agent-lab/tree/main/references/initial-sendbox/skills/agentic-eval
Command: npx skills add https://github.com/arielybarak/agent-lab --skill agentic-eval-arielybarak

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of evaluating and improving the quality of AI agent outputs through iterative processes and structured feedback.

Core Features & Use Cases

  • Self-Critique and Reflection Loops: Implements self-critique mechanisms to enhance agent outputs.
  • Evaluator-Optimizer Pipelines: Separates generation and evaluation for clear responsibilities and optimized performance.
  • Code-Specific Reflection: Integrates test-driven refinement loops for code generation.
  • Evaluation Strategies: Offers various strategies such as outcome-based, LLM-as-Judge, and rubric-based evaluation.
  • Best Practices: Provides guidelines for clear criteria, iteration limits, convergence checks, and structured output.
  • Quick Start: Implement evaluation and optimization for AI agent outputs by defining criteria, setting up an evaluation loop, and refining based on feedback.

Quick Start

Implement a self-critique loop for AI agent outputs by defining evaluation criteria, generating an initial output, and iteratively refining based on feedback.

Frequently Asked Questions about agentic-eval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement self-critique loops to improve AI agent outputs?▼

Evaluator-optimizer pipelines separate generation and evaluation responsibilities to optimize AI agent performance. Various evaluation strategies like outcome-based, LLM-as-Judge, and rubric-based evaluation offer structured feedback for iterative refinement.

What is the best way to evaluate AI agent code generation quality?▼

Best practices for iterative AI agent evaluation include setting clear criteria, enforcing iteration limits, applying convergence checks, and using structured output formats. These guidelines ensure quality improvement while preventing infinite loops.

How does LLM-as-Judge evaluation work for iterative agent refinement?▼

Yes, you can apply test-driven refinement loops to AI agent code generation. This code-specific reflection integrates tests into the evaluation loop to iteratively verify and improve generated code outputs.

What are the best practices for setting iteration limits in AI evaluation?▼

You need structured feedback mechanisms and iterative optimization techniques to use this evaluation approach. These components are required to assess and enhance AI agent outputs through continuous refinement loops.

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