Evaluation & Testing

AI workflow evaluation is like quality control for intelligent automation. Unlike traditional workflows where you can predict exact outputs, AI systems need testing to ensure they behave correctly across different scenarios and edge cases.

Think of it as the difference between testing a calculator (predictable results) and testing a human assistant (need to verify judgment and reasoning).

Why evaluation matters

AI workflows can behave differently with different inputs, making systematic testing crucial:

Traditional Testing

Input: User clicks button Expected: Form submits Test: Click button, verify form submission Result: Pass/Fail (predictable)

AI Testing

Input: “Find competitor pricing” Expected: Accurate pricing data Test: Multiple competitor sites, various layouts Result: Accuracy score, edge case handling (variable)

Additional considerations:

• Does it handle different website layouts?
• Can it recognize pricing in various formats?
• How does it behave with missing information?

Types of AI evaluation

Accuracy testing

Measuring how often the AI gets the right answer:

graph LR
    Input[Test Input] --> AI[AI System]
    AI --> Output[AI Output]
    Output --> Compare[Compare to Expected]
    Compare --> Score[Accuracy Score]
    
    style Compare fill:#6d28d9,stroke:#fff,color:#fff

Example: Content extraction workflow

• Test on 100 different product pages
• Measure how often it correctly extracts price, title, description
• Calculate accuracy percentage for each field

Robustness testing

How well the AI handles unexpected or challenging inputs:

Test scenarios:

• Broken or malformed web pages
• Content in different languages
• Missing or incomplete information
• Unusual formatting or layouts

Performance testing

Measuring speed, resource usage, and scalability:

Metrics to track:

• Processing time per task
• Memory usage during execution
• Success rate under load
• Error recovery time

Evaluation strategies

1. Create test datasets: Collect representative examples of inputs and expected outputs

2. Define success criteria: What constitutes “good enough” performance for your use case

3. Build evaluation workflows: Automate the testing process using Agentic WorkFlow

4. Monitor continuously: Set up ongoing evaluation as your workflows run in production

5. Iterate and improve: Use evaluation results to refine and optimize your workflows

Building evaluation workflows

Automated testing pipeline

Create workflows that test your AI workflows:

graph TD
    TestData[Test Dataset] --> RunTest[Execute AI Workflow]
    RunTest --> Collect[Collect Results]
    Collect --> Compare[Compare to Expected]
    Compare --> Score[Calculate Scores]
    Score --> Report[Generate Report]
    
    style RunTest fill:#e1f5fe
    style Compare fill:#e8f5e8
    style Score fill:#fff3e0

A/B testing for AI

Compare different approaches to see which works better:

Example: Content summarization

• Version A: Basic LLM Chain with simple prompt
• Version B: Q&A Node with structured questions
• Test: Same 50 articles through both versions
• Measure: Summary quality, accuracy, user satisfaction

Human evaluation integration

Some aspects need human judgment:

Automated metrics:

• Accuracy (correct/incorrect)
• Speed (processing time)
• Coverage (percentage of tasks completed)

Human evaluation:

• Content quality and readability
• Appropriateness of responses
• User experience and satisfaction

Real-world evaluation examples

E-commerce data extraction

Goal: Extract product information from various shopping sites

Test approach:

1. Dataset: 200 product pages from 10 different sites
2. Metrics: Accuracy of price, title, description, image extraction
3. Edge cases: Sale prices, out-of-stock items, different currencies
4. Success criteria: 95% accuracy on price, 90% on other fields

Customer support automation

Goal: Automatically categorize and route support tickets

Test approach:

1. Dataset: 1000 historical support tickets with known categories
2. Metrics: Classification accuracy, response appropriateness
3. Edge cases: Ambiguous requests, multiple issues in one ticket
4. Success criteria: 85% correct classification, 90% user satisfaction

Content quality assessment

Goal: Automatically evaluate article quality for publication

Test approach:

1. Dataset: 500 articles with human quality ratings
2. Metrics: Correlation with human ratings, consistency
3. Edge cases: Different content types, various writing styles
4. Success criteria: 80% agreement with human evaluators

Evaluation metrics

Quantitative metrics

Numbers you can measure directly:

Metric	Meaning	Best For
Accuracy	Did it get the right answer?	Categorizing items, extracting data
Relevance	Was the answer helpful even if not perfect?	Search, recommendations
Completeness	Did it find all the relevant items?	Research, data collection

Qualitative metrics

Aspects that require human judgment:

Metric	Purpose	Evaluation Method
Relevance	How well results match user intent	Human rating scales
Coherence	Logical flow and consistency	Expert evaluation
Usefulness	Practical value to users	User feedback surveys
Safety	Avoiding harmful outputs	Content review processes

Start with simple metrics

Begin with basic accuracy measurements and gradually add more sophisticated evaluation criteria as you understand your workflow’s behavior better.

Continuous evaluation

Production monitoring

Keep evaluating workflows as they run in real environments:

Monitoring approaches:

• Sample a percentage of production runs for evaluation
• Track user feedback and satisfaction scores
• Monitor error rates and failure patterns
• Compare performance over time

Feedback loops

Use evaluation results to improve workflows:

graph LR
    Deploy[Deploy Workflow] --> Monitor[Monitor Performance]
    Monitor --> Evaluate[Evaluate Results]
    Evaluate --> Insights[Generate Insights]
    Insights --> Improve[Improve Workflow]
    Improve --> Deploy
    
    style Evaluate fill:#6d28d9,stroke:#fff,color:#fff
    style Improve fill:#e8f5e8

Common evaluation challenges

Defining “correct” answers

• AI outputs can be subjective or have multiple valid answers
• Need clear criteria for what constitutes success
• Consider using ranges or confidence scores instead of binary pass/fail

Representative test data

• Test data must reflect real-world usage patterns
• Include edge cases and unusual scenarios
• Keep test data updated as your use cases evolve

Evaluation bias

• Human evaluators may have unconscious biases
• Test data might not represent all user groups
• Consider multiple evaluation perspectives and methods

Scalability of evaluation

• Manual evaluation doesn’t scale to large datasets
• Need automated evaluation methods for continuous monitoring
• Balance between evaluation thoroughness and resource costs

Systematic evaluation ensures your AI workflows perform reliably and improve over time, building confidence in automated intelligent systems.