Building Reliable AI Agents: A Developer's Guide to Testing and Evaluation

A practical guide to developing dependable AI agents, focusing on evaluation methods, testing strategies, and best practices drawn from real-world experience.

The rise of Large Language Models (LLMs) has revolutionized AI development, but with this power comes a critical challenge: ensuring reliability and performance. This guide distills years of hands-on experience into practical strategies for building dependable AI agents.

The Foundation: Start with Evaluation

The most common mistake in AI development is rushing to build before establishing proper evaluation methods. Here's why this matters and how to do it right:

Core Principles

1. Start Small, Start Right

   • Begin with the smallest possible version of your task
   • Create manual examples before writing any code
   • Build your evaluation framework before expanding features

2. Ground Truth is Gold

   • Manually complete your task multiple times
   • Document edge cases as you discover them
   • Create a diverse set of test cases

3. Measure Everything

   • Track token usage for efficiency
   • Monitor response times and costs
   • Log all prompt variations and their outcomes

The Developer's Toolkit

Essential Tools

1. Evaluation Frameworks

   • Ragas: Open-source evaluation for LLM applications
   • LangSmith: Testing and monitoring infrastructure
   • OpenAI Evals: Standardized evaluation methods

2. Development Tools

   • LangChain: Building complex LLM applications
   • Guidance: Structured prompt engineering
   • Gradio: Rapid UI prototyping

3. Monitoring & Optimization

   • tiktoken: Token usage optimization
   • Weights & Biases: Experiment tracking
   • Helicone: Usage monitoring and cost analysis

Practical Implementation Guide

Step 1: Initial Setup (20 minutes)

# Core evaluation framework
def setup_eval_framework():
    return {
        'test_cases': [],
        'metrics': {'accuracy': 0, 'token_usage': 0},
        'experiments': []
    }

# Basic experiment logger
def log_experiment(hypothesis, prompt, results):
    return {
        'date': datetime.now(),
        'hypothesis': hypothesis,
        'prompt': prompt,
        'results': results,
        'observations': []
    }

Step 2: Prompt Engineering (20 minutes)

• Start with minimal prompts
• Optimize token usage
• Document variations systematically

Step 3: Testing & Iteration (20 minutes)

• Run standardized test cases
• Analyze failures
• Iterate on prompt design

Best Practices and Common Pitfalls

Do:

• Create evaluations before building features
• Keep detailed experiment logs
• Use deterministic solutions when possible

Don't:

• Skip manual testing
• Ignore token optimization
• Overlook edge cases

Common Patterns for Success

1. The Evaluation First Pattern

   Define Success → Create Test Cases → Build → Evaluate → Iterate
   

2. The Token Optimization Loop

   Draft Prompt → Count Tokens → Optimize → Test → Repeat
   

3. The Ground Truth Pipeline

   Manual Examples → Edge Cases → Test Suite → Automated Testing
   

Practical Walkthroughs

Part 1: Initial Setup & Evaluation Framework (20 minutes)

Let's build a sentiment classifier for product reviews as a concrete example.

1. Define Your Minimal Task

# task_definition.py
TASK_DESCRIPTION = """
Task: Classify product review sentiment
Input: Single-sentence review
Output: POSITIVE or NEGATIVE only
Success Metric: >90% accuracy on test cases
"""

2. Create Manual Examples

# manual_examples.py
TRAINING_EXAMPLES = [
    {"review": "The battery life on this laptop is amazing!", "sentiment": "POSITIVE"},
    {"review": "Broke after just two weeks of normal use", "sentiment": "NEGATIVE"},
    {"review": "Very comfortable shoes, worth every penny", "sentiment": "POSITIVE"},
    {"review": "Terrible customer service, never responded", "sentiment": "NEGATIVE"},
    {"review": "Setup was quick and interface is intuitive", "sentiment": "POSITIVE"}
]

TEST_CASES = [
    {
        "review": "Product arrived damaged and customer service ignored me",
        "sentiment": "NEGATIVE",
        "reasoning": "Reports both product and service issues"
    },
    {
        "review": "Perfect fit and color exactly as shown online",
        "sentiment": "POSITIVE",
        "reasoning": "Meets expectations on multiple criteria"
    },
    {
        "review": "Stopped working after the first wash",
        "sentiment": "NEGATIVE",
        "reasoning": "Clear product failure"
    }
]

3. Setup Evaluation Framework

# evaluation.py
from datetime import datetime
import json

class ExperimentTracker:
    def __init__(self):
        self.experiments = []
        self.current_experiment = None
    
    def start_experiment(self, hypothesis):
        self.current_experiment = {
            'id': len(self.experiments) + 1,
            'date': datetime.now().isoformat(),
            'hypothesis': hypothesis,
            'test_results': [],
            'accuracy': 0.0,
            'token_usage': 0,
            'observations': []
        }
    
    def log_result(self, test_case, prediction, tokens_used):
        if not self.current_experiment:
            raise Exception("No active experiment")
            
        result = {
            'test_case': test_case,
            'prediction': prediction,
            'correct': prediction == test_case['sentiment'],
            'tokens': tokens_used
        }
        self.current_experiment['test_results'].append(result)
    
    def end_experiment(self, observations):
        if not self.current_experiment:
            return
            
        results = self.current_experiment['test_results']
        correct = sum(1 for r in results if r['correct'])
        total = len(results)
        
        self.current_experiment['accuracy'] = correct / total
        self.current_experiment['observations'] = observations
        self.experiments.append(self.current_experiment)
        self.current_experiment = None
    
    def export_results(self, filename):
        with open(filename, 'w') as f:
            json.dump(self.experiments, f, indent=2)

Part 2: Prompt Engineering & Token Optimization (20 minutes)

1. Initial Prompt Design

# prompts.py
from typing import Dict
import tiktoken

class PromptManager:
    def __init__(self):
        self.tokenizer = tiktoken.get_encoding("cl100k_base")
        self.prompts = {}
    
    def create_prompt(self, name: str, template: str, examples: list):
        prompt = {
            'template': template,
            'examples': examples,
            'token_count': len(self.tokenizer.encode(template))
        }
        self.prompts[name] = prompt
        return prompt
    
    def get_formatted_prompt(self, name: str, input_text: str) -> Dict[str, any]:
        if name not in self.prompts:
            raise KeyError(f"Prompt '{name}' not found")
            
        prompt = self.prompts[name]
        formatted = prompt['template'].format(
            examples=self.format_examples(prompt['examples']),
            input=input_text
        )
        
        return {
            'text': formatted,
            'tokens': len(self.tokenizer.encode(formatted))
        }
    
    @staticmethod
    def format_examples(examples):
        return "\n".join([
            f'Review: "{ex["review"]}" → {ex["sentiment"]}'
            for ex in examples[:3]  # Use only first 3 examples
        ])

# Create prompt variations
prompt_manager = PromptManager()

# Minimal Prompt
prompt_manager.create_prompt(
    "minimal",
    "Classify review sentiment. Output POSITIVE or NEGATIVE only.\n\n{examples}\n\nReview: {input}\nSentiment:",
    TRAINING_EXAMPLES
)

# Examples-focused Prompt
prompt_manager.create_prompt(
    "examples_focused",
    "Learn from these examples:\n{examples}\n\nClassify the following review as POSITIVE or NEGATIVE:\n{input}\nSentiment:",
    TRAINING_EXAMPLES
)

# Rule-focused Prompt
prompt_manager.create_prompt(
    "rules_focused",
    """Rules:
- If review mentions satisfaction/quality/durability = POSITIVE
- If review mentions problems/defects/disappointment = NEGATIVE

{examples}

Review: {input}
Sentiment:""",
    TRAINING_EXAMPLES
)

Part 3: Testing & Iteration (20 minutes)

1. Testing Framework

# testing.py
from typing import List, Dict
from langchain import LLMChain, PromptTemplate
import asyncio

class SentimentTester:
    def __init__(self, llm, prompt_manager, experiment_tracker):
        self.llm = llm
        self.prompt_manager = prompt_manager
        self.tracker = experiment_tracker
    
    async def test_prompt_variation(self, 
                                  prompt_name: str, 
                                  test_cases: List[Dict], 
                                  hypothesis: str):
        self.tracker.start_experiment(hypothesis)
        
        for test_case in test_cases:
            prompt_info = self.prompt_manager.get_formatted_prompt(
                prompt_name, 
                test_case['review']
            )
            
            response = await self.llm.agenerate([prompt_info['text']])
            prediction = response.generations[0][0].text.strip()
            
            self.tracker.log_result(
                test_case,
                prediction,
                prompt_info['tokens']
            )
        
        self.tracker.end_experiment([
            f"Tested prompt variation: {prompt_name}",
            f"Total tokens used: {sum(r['tokens'] for r in self.tracker.current_experiment['test_results'])}"
        ])

# Usage example
async def run_tests(llm):
    prompt_manager = PromptManager()
    tracker = ExperimentTracker()
    tester = SentimentTester(llm, prompt_manager, tracker)
    
    # Test each prompt variation
    for prompt_name in ["minimal", "examples_focused", "rules_focused"]:
        await tester.test_prompt_variation(
            prompt_name,
            TEST_CASES,
            f"Testing if {prompt_name} prompt achieves >90% accuracy"
        )
    
    # Export results
    tracker.export_results("sentiment_classification_results.json")

# Run the tests
if __name__ == "__main__":
    from langchain.chat_models import ChatOpenAI
    llm = ChatOpenAI(temperature=0)
    asyncio.run(run_tests(llm))

2. Analysis & Iteration

# analysis.py
import pandas as pd
import matplotlib.pyplot as plt

def analyze_results(results_file: str):
    with open(results_file) as f:
        data = json.load(f)
    
    df = pd.DataFrame([{
        'prompt_type': exp['hypothesis'].split()[2],
        'accuracy': exp['accuracy'],
        'avg_tokens': sum(r['tokens'] for r in exp['test_results']) / len(exp['test_results'])
    } for exp in data])
    
    # Plot results
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
    
    df.plot(kind='bar', y='accuracy', ax=ax1, title='Accuracy by Prompt Type')
    df.plot(kind='bar', y='avg_tokens', ax=ax2, title='Average Tokens by Prompt Type')
    
    plt.tight_layout()
    plt.savefig('prompt_comparison.png')
    
    return df

This expanded section provides concrete implementations that developers can use as a starting point. The code is structured to be modular and extensible, making it easy to adapt for different use cases.

Getting Started

1. Choose your core tools:

   • One evaluation framework
   • One development framework
   • One monitoring solution

2. Create your initial workflow:

   • Set up experiment tracking
   • Define success metrics
   • Create basic test cases

3. Build your first agent:

   • Start with a minimal implementation
   • Focus on reliability over features
   • Iterate based on evaluation results

Conclusion

Building reliable AI agents isn't just about the code—it's about establishing robust evaluation methods and maintaining disciplined development practices. By following these guidelines and using the right tools, you can create more dependable AI applications while avoiding common pitfalls.

Remember: The time invested in proper evaluation and testing will save multiples in debugging and maintenance later.

Resources for Further Learning

• LangChain Documentation
• OpenAI Evals Framework
• Weights & Biases Guides