agentic-eval | SkillBundle

agentic-eval | SkillBundle

Install

npx skills add github/awesome-copilot --skill agentic-eval

Overview

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Documentation

Agentic Evaluation Patterns

Patterns for self-improvement through iterative evaluation and refinement.

Overview

Evaluation patterns enable agents to assess and improve their own outputs, moving beyond single-shot generation to iterative refinement loops.

Generate → Evaluate → Critique → Refine → Output
    ↑                              │
    └──────────────────────────────┘

When to Use

Quality-critical generation: Code, reports, analysis requiring high accuracy
Tasks with clear evaluation criteria: Defined success metrics exist
Content requiring specific standards: Style guides, compliance, formatting

Pattern 1: Basic Reflection

Agent evaluates and improves its own output through self-critique.

def reflect_and_refine(task: str, criteria: list[str], max_iterations: int = 3) -> str:

Key insight: Use structured JSON output for reliable parsing of critique results.

Pattern 2: Evaluator-Optimizer

Separate generation and evaluation into distinct components for clearer responsibilities.

Pattern 3: Code-Specific Reflection

Test-driven refinement loop for code generation.

Evaluation Strategies

Outcome-Based

Evaluate whether output achieves the expected result.

LLM-as-Judge

Use LLM to compare and rank outputs.

Rubric-Based

Score outputs against weighted dimensions.

Best Practices

Practice	Rationale
Clear criteria	Define specific, measurable evaluation criteria upfront
Iteration limits	Set max iterations (3-5) to prevent infinite loops
Convergence check	Stop if output score isn't improving between iterations
Log history	Keep full trajectory for debugging and analysis
Structured output	Use JSON for reliable parsing of evaluation results

Quick Start Checklist

class EvaluatorOptimizer:
    def __init__(self, score_threshold: float = 0.8):
        self.score_threshold = score_threshold
    
    def generate(self, task: str) -> str:
        return llm(f"Complete: {task}")
    
    def evaluate(self, output: str, task: str) -> dict:
        return json.loads(llm(f"""
        Evaluate output for task: {task}
        Output: {output}
        Return JSON: {{"overall_score": 0-1, "dimensions": {{"accuracy": ..., "clarity": ...}}}}
        """))
    
    def optimize(self, output: str, feedback: dict) -> str:
        return llm(f"Improve based on feedback: {feedback}\nOutput: {output}")
    
    def run(self, task: str, max_iterations: int = 3) -> str:
        output = self.generate(task)
        for _ in range(max_iterations):
            evaluation = self.evaluate(output, task)
            if evaluation["overall_score"] >= self.score_threshold:
                break
            output = self.optimize(output, evaluation)
        return output

class CodeReflector:
    def reflect_and_fix(self, spec: str, max_iterations: int = 3) -> str:
        code = llm(f"Write Python code for: {spec}")
        tests = llm(f"Generate pytest tests for: {spec}\nCode: {code}")
        
        for _ in range(max_iterations):
            result = run_tests(code, tests)
            if result["success"]:
                return code
            code = llm(f"Fix error: {result['error']}\nCode: {code}")
        return code

def evaluate_outcome(task: str, output: str, expected: str) -> str:
    return llm(f"Does output achieve expected outcome? Task: {task}, Expected: {expected}, Output: {output}")

def llm_judge(output_a: str, output_b: str, criteria: str) -> str:
    return llm(f"Compare outputs A and B for {criteria}. Which is better and why?")

RUBRIC = {
    "accuracy": {"weight": 0.4},
    "clarity": {"weight": 0.3},
    "completeness": {"weight": 0.3}
}
 
def evaluate_with_rubric(output: str, rubric: dict) -> float:
    scores = json.loads(llm(f"Rate 1-5 for each dimension: {list(rubric.keys())}\nOutput: {output}"))
    return sum(scores[d] * rubric[d]["weight"] for d in rubric) / 5

## Evaluation Implementation Checklist
 
### Setup
- [ ] Define evaluation criteria/rubric
- [ ] Set score threshold for "good enough"
- [ ] Configure max iterations (default: 3)
 
### Implementation
- [ ] Implement generate() function
- [ ] Implement evaluate() function with structured output
- [ ] Implement optimize() function
- [ ] Wire up the refinement loop
 
### Safety
- [ ] Add convergence detection
- [ ] Log all iterations for debugging
- [ ] Handle evaluation parse failures gracefully