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:
    """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.

Pattern 2: Evaluator-Optimizer

Separate generation and evaluation into distinct components for clearer responsibilities.

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

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

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())
how to use agentic-eval
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use agentic-eval on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add agentic-eval
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/github/awesome-copilot --skill agentic-evalcopy
The skills CLI fetches agentic-eval from GitHub repository github/awesome-copilot and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/agentic-eval
Reload or restart Cursor to activate agentic-eval. Access the skill through slash commands (e.g., /agentic-eval) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?