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
• Iterative improvement workflows: Drafts, plans, prompts, or code need critique and refinement before delivery

Do Not Use This Skill For

• Formal compliance claims, certification, or audit sign-off without approved organisational evidence
• Production monitoring design, runtime governance, access control, or policy enforcement
• Broad statistical evaluation methodology, benchmark design, or model selection studies
• Security, privacy, or safety review where evaluation loops are not the central concern

Expected Outputs

When applying this skill, produce the assets needed to run and inspect an evaluation loop:

• evaluation criteria or rubric
• generator, evaluator, and optimiser responsibilities
• iteration limit and convergence policy
• structured evaluator output format
• failure handling for malformed evaluations or non-improving outputs
• logging fields for debugging and review
• final recommendation or refined output with evaluation history summary

Treat code blocks in this skill as adaptable Python-style skeletons. Replace llm, run_tests, model settings, and logging functions with project-specific implementations.

Pattern 1: Basic Reflection

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

import json


def reflect_and_refine(task: str, criteria: list[str], max_iterations: int = 3) -> str:
    """Generate with reflection loop."""
    output = llm(f"Complete this task:\n{task}")
    
    for i in range(max_iterations):
        # Self-critique
        critique = llm(f"""
        Evaluate this output against criteria: {criteria}
        Output: {output}
        Rate each: PASS/FAIL with feedback as JSON.
        """)
        
        critique_data = json.loads(critique)
        all_pass = all(c["status"] == "PASS" for c in critique_data.values())
        if all_pass:
            return output
        
        # Refine based on critique
        failed = {k: v["feedback"] for k, v in critique_data.items() if v["status"] == "FAIL"}
        output = llm(f"Improve to address: {failed}\nOriginal: {output}")
    
    return output

Key insight: Use structured JSON output for reliable parsing of critique results, and handle parsing failures as evaluation failures rather than silently accepting the draft.

Pattern 2: Evaluator-Optimizer

Separate generation and evaluation into distinct components for clearer responsibilities.

import json


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)
        previous_score = 0.0
        for _ in range(max_iterations):
            evaluation = self.evaluate(output, task)
            current_score = evaluation["overall_score"]
            if current_score >= self.score_threshold:
                break
            if current_score <= previous_score:
                break
            previous_score = current_score
            output = self.optimize(output, evaluation)
        return output

Pattern 3: Code-Specific Reflection

Test-driven refinement loop for code generation.

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

Evaluator Reliability

Evaluation loops are only useful when the evaluator is harder to fool than the generator. For high-risk or quality-critical work:

• Anchor the evaluator with a concrete rubric and examples of passing and failing outputs.
• Ask for evidence, failure reasons, and uncertainty, not just a score.
• Randomise or blind comparison order when comparing alternatives to reduce position bias.
• Repeat judging or use multiple evaluators when scores are close or the decision is consequential.
• Escalate to human review when evaluator outputs conflict, confidence is low, or policy-sensitive content is involved.
• Track whether scores improve over iterations; stop when improvement stalls to avoid overfitting to the evaluator.

Evaluation Strategies

Outcome-Based

Evaluate whether output achieves the expected result.

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

LLM-as-Judge

Use LLM to compare and rank outputs.

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-Based

Score outputs against weighted dimensions.

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

Best Practices

Failure Handling

Plan for evaluation failures before running the loop:

• If evaluator JSON is malformed, retry once with the schema and then fail closed with the raw response logged.
• If the evaluator gives no evidence, treat the score as unreliable and request evidence before refining.
• If output quality stops improving, return the best-scored candidate and include the stopping reason.
• If evaluation criteria conflict, pause and ask for priority order rather than optimising against incompatible goals.
• If generated tests or checks are themselves suspect, review or regenerate the tests before trusting the score.

Quick Start Checklist

## 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
- [ ] Escalate low-confidence or high-impact decisions to human review