How to test AI agents

Introduction

Testing AI agents differs significantly from traditional software testing due to the inherent variability of AI-generated responses. Conventional software follows a deterministic approach where a given input produces a predictable output. In contrast, AI systems—particularly those powered by large language models (LLMs)—exhibit probabilistic behavior, meaning the same input can yield different results depending on factors such as temperature settings, prompt variations, and model updates.

This non-deterministic nature creates unique challenges: How can we ensure consistency and reliability in AI agent performance when outputs fluctuate? Manual testing is impractical due to the sheer volume of interactions and the subjective nature of human evaluation. Instead, an automated and statistical approach is required to track performance trends, detect regressions, and make data-driven improvements.

The need for automated AI testing arises for several key reasons:

• Handling Variability: AI responses are not static, requiring multiple test runs, statistical analysis, and trend tracking for meaningful evaluation.
• Scalability: AI agents handle vast numbers of interactions, making manual verification infeasible. Automation enables the efficient assessment of large datasets.
• Continuous Improvement: AI models evolve over time, with prompt refinements, fine-tuning, and model upgrades affecting outcomes. Automated evaluation ensures that changes lead to measurable improvements.
• Reproducibility: Unlike traditional unit testing, AI evaluation relies on statistical principles. Automated testing allows systematic re-execution of test cases to validate enhancements consistently.

This article explores best practices for AI agent testing, covering strategies for isolating testable components, designing statistical experiments, and classifying AI-generated outputs effectively. By leveraging automation, we can develop AI systems that are not only powerful but also reliable, scalable, and continuously improving.

Split the Agent Calls

Testing AI agents effectively starts with decomposing their operations into distinct parts. Many AI-driven systems are a combination of deterministic and non-deterministic processes. By identifying and isolating deterministic components, we can create reliable unit tests that validate expected behaviors while leaving non-deterministic evaluation to statistical methods.

Deterministic vs. Non-Deterministic Components

Deterministic tools produce predictable outputs for the same given inputs. These include structured API calls, database queries, and calculations. In contrast, non-deterministic components, such as Large Language Models (LLMs), generate varied responses based on factors like randomness, prompt phrasing, and model updates. Understanding this distinction is critical when designing a test strategy.

Examples of Deterministic Components:

• Tool Calls: When an LLM makes an API request to a specific service (e.g., retrieving the weather for a given city), the output should always be the same for the same input.
• Mathematical Calculations: If an AI agent processes a numerical computation, the expected result should remain consistent.
• Structured Data Processing: If an AI extracts key-value pairs from structured input (e.g., JSON parsing or SQL queries), the results should be verifiable.

Examples of Non-Deterministic Components:

• Text Generation: LLMs generating responses can yield different outputs even when given identical prompts.
• Summarization & Classification: The same input text might be summarized or categorized differently depending on temperature settings or minor prompt tweaks.
• Reasoning & Decision-Making: LLM-based reasoning can vary, especially in complex problem-solving tasks.

Identifying Testable Deterministic Parts

Since AI agents often involve a chain of calls between deterministic and non-deterministic components, it is crucial to extract and isolate deterministic sections for unit testing. Consider the following approach:

1. Break Down the Workflow
   • Analyze the flow of agent execution.
   • Identify which parts involve LLM decisions and which involve tool execution.
2. Extract Deterministic Subtasks
   • If an LLM invocation results in a structured tool call (e.g., retrieving customer information from a database), validate that the correct API is being triggered with the expected parameters.
   • If an LLM generates SQL queries or structured commands, extract and test those separately.
3. Generate and Collect Test Cases
   • Build test cases where given inputs must result in predefined, expected outputs.
   • Use mock services to simulate API calls, ensuring that specific inputs always produce the correct tool execution.

By isolating the LLM’s role in structuring this API request and validating that the same user input always leads to the expected API call, we can test this aspect deterministically.

By focusing on deterministic sections of AI agent workflows, we can construct robust unit tests that provide consistent validation. This lays the foundation for handling the more challenging non-deterministic aspects, which we will explore in later sections.

Every Change in the Prompt/Model Should Be Considered as a Statistical Experiment

When working with AI agents, every modification—whether it’s a change in the prompt, a model update, or an adjustment to system parameters—should be treated as a statistical experiment. Unlike traditional software, where a change can be verified with deterministic assertions, AI models operate probabilistically. This means that evaluating changes requires statistical analysis rather than simple pass/fail tests.

Understanding AI Performance as a Statistical Distribution

Since AI-generated responses are not fixed, we must evaluate their effectiveness using statistical methods. Given a fixed input, an AI model can produce different outputs across multiple runs. The key to assessing the impact of changes is to measure how these variations affect performance over time.

Every AI system operates within a performance distribution. When we modify a prompt or upgrade a model, we are effectively shifting that distribution. The goal of testing is to determine whether this shift improves, degrades, or maintains performance.

Measuring Success Rates and Confidence Levels

To evaluate an AI change, we measure the success rate—the proportion of test cases that meet predefined criteria—and compute a confidence level to determine if the observed results are statistically significant.

1. Run Multiple Trials – Since AI models produce variable outputs, a single run is insufficient. Instead, execute a statistically significant number of test cases to capture a distribution of outcomes.
2. Define Success Criteria – Establish objective metrics for evaluating responses. These could include accuracy in structured outputs, coherence in text generation, or adherence to predefined patterns.
3. Compute Statistical Significance – Use statistical tests (e.g., A/B testing, hypothesis testing) to determine whether a performance change is meaningful or due to random variation.

Statistical Experiment Design for AI Testing

To analyze the impact of a change effectively, follow these steps:

• Baseline Collection: Before making any changes, gather performance metrics from a sufficient number of test cases. This provides a control group against which new results can be compared.
• Controlled Experiment: Introduce the change (e.g., modify a prompt, fine-tune a model) and run the same set of test cases, ensuring consistent conditions.
• Compare Distributions: Use statistical methods such as:
  • Confidence Intervals: To estimate the range in which performance is likely to fall.
  • t-tests or Chi-Square Tests: To compare two distributions and assess whether the change is statistically significant.
  • A/B Testing: To evaluate differences between the original and modified model over multiple trials.

Example: Evaluating a Prompt Change

Suppose an AI agent is responsible for extracting key information from customer support emails. The original prompt achieves an accuracy of 85% across 500 test cases. A new, refined prompt is introduced, and its accuracy is measured at 88% over another 500 cases. While this appears to be an improvement, we must assess whether this difference is statistically significant.

• Null Hypothesis (H₀): The new prompt does not significantly improve accuracy.
• Alternative Hypothesis (H₁): The new prompt improves accuracy.
• Applying a statistical test: If a hypothesis test (e.g., a chi-square test) shows a p-value less than 0.05, we can conclude with 95% confidence that the improvement is real and not due to random variation.

Practical Implementation in Automated AI Testing

Automating this process ensures continuous monitoring and data-driven decision-making. Tools like Monte Carlo simulations, bootstrapping, and Bayesian analysis can enhance confidence in results. By integrating statistical evaluation into CI/CD pipelines, organizations can:

• Detect regressions when model updates introduce unexpected changes.
• Validate whether a prompt adjustment meaningfully improves performance.
• Quantify improvements instead of relying on anecdotal observations.

How to Calculate the Experiment Results (Crash Course in Statistics)

When testing AI agents, evaluating changes requires statistical rigor to ensure that observed differences in performance are meaningful and not due to random fluctuations. This section provides a concise guide to key statistical concepts and formulas necessary for analyzing AI test results.

1. The Importance of Sample Size

The reliability of test results depends on the number of test cases (sample size). A small sample size can lead to misleading conclusions due to high variance, while a sufficiently large sample size allows for more accurate estimates of performance changes.

The Margin of Error (MOE) is given by:

MOE = Z × (σ / √n)

Where:

• Z is the Z-score for the desired confidence level (e.g., 1.96 for 95%)

• σ is the standard deviation of the results

• n is the sample size

To reduce uncertainty, increase n. The law of large numbers ensures that as n grows, the estimated performance converges to the true value.

2. Confidence Intervals

A confidence interval estimates the range in which the true performance metric is likely to fall.

For a proportion-based metric (e.g., accuracy rate p):

CI = p ± Z × √(p(1 – p) / n)

If we measure an AI agent’s accuracy at 85% over 1000 test cases, the 95% confidence interval is:

0.85 ± 1.96 × √(0.85 × 0.15 / 1000)

= 0.85 ± 0.022

= [82.8%, 87.2%]

This means we are 95% confident that the true accuracy lies within this range.

3. Hypothesis Testing

To determine if a change (e.g., prompt modification) significantly improves performance, we use hypothesis testing:

• Null Hypothesis (H₀): The change has no effect.

• Alternative Hypothesis (H₁): The change improves performance.

Using a two-sample t-test for means:

t = (x̄₁ – x̄₂) / √((s₁² / n₁) + (s₂² / n₂))

Where:

• x̄₁, x̄₂ are the mean performances before and after the change

• s₁, s₂ are the standard deviations

• n₁, n₂ are the sample sizes

If p < 0.05, we reject H₀, indicating a significant improvement.

4. A/B Testing for AI Agents

A/B testing helps compare two AI configurations by running them in parallel and measuring success rates.

For two groups with success rates p_A and p_B:

Z = (p_A – p_B) / √(p(1 – p) × (1/n_A + 1/n_B))

Where p is the pooled proportion:

p = (x_A + x_B) / (n_A + n_B)

If Z exceeds the critical value for the desired confidence level, we conclude a statistically significant difference.

Non-Deterministic Tools: Using LLMs to Classify Results

Testing AI agents presents a unique challenge because non-deterministic tools, such as Large Language Models (LLMs), generate varied outputs for the same input. Unlike deterministic systems, where expected outputs can be directly compared to actual results, evaluating AI-generated responses requires a more nuanced approach. One effective solution is to use another LLM (or the same LLM) as a classifier to assess responses against predefined criteria. This method transforms an inherently probabilistic process into structured, automated validation that enables statistical analysis.

Why Use an LLM for Classification?

Since AI outputs can vary significantly, direct string comparisons are ineffective. Instead, leveraging an LLM for evaluation provides several advantages:

• Scalability: Automates response evaluation across large datasets without manual intervention.

• Consistency: Eliminates subjective human bias by applying the same evaluation criteria uniformly.

• Adaptability: Allows flexible testing by adjusting classification prompts or response guidelines.

• Standardization: Converts diverse outputs into structured pass/fail results, enabling statistical trend analysis.

By using LLM-based classification, organizations can effectively validate AI-generated responses while maintaining automation and reliability.

Implementing LLM-Based Classification

To systematically classify AI outputs, follow these steps:

1. Define Clear Evaluation Criteria

Before assessing responses, establish well-defined criteria for success. These may include:

• Exact Match: If a structured output is expected (e.g., JSON format), results must be identical.

• Semantic Accuracy: The response must convey the correct meaning, even if phrased differently.

• Compliance with Guidelines: Responses should adhere to specified tone, style, or formatting requirements.

2. Generate and Collect Multiple Outputs

Since LLM responses are inherently variable, each test case should be executed multiple times to capture a representative sample. This helps in detecting inconsistencies or regressions across different runs.

3. Use an LLM to Classify Responses

Instead of manually evaluating outputs, pass the generated responses to an LLM acting as a classifier. The classification prompt should include:

• The original input/query

• Expected characteristics of a correct response

• The AI-generated response

• A direct classification request (e.g., “Does this response correctly answer the question? Output ‘PASS’ or ‘FAIL’.”)

Example Classification Prompt:

Instruction to Classifier Model:

Evaluate the following response based on the provided criteria.

• Input: “Summarize the key points of the given article.”

• Expected Response Criteria: The summary should concisely capture the main arguments and conclusions.

• AI-Generated Response: “The article discusses AI testing methodologies, emphasizing statistical validation. It also covers deterministic vs. non-deterministic tools.”

Question: Does this response meet the expected criteria? Respond with “PASS” or “FAIL”.

By automating this classification step, we can ensure consistent and objective response evaluation.

4. Aggregate and Analyze Classification Results

Once the LLM classifier processes multiple test cases, aggregate the results to derive meaningful insights. Key performance indicators include:

• Pass Rate: Percentage of responses classified as “PASS.”

• Response Variability: Differences in classification outcomes across multiple runs.

• Error Patterns: Recurring failure types that indicate potential model weaknesses.

These insights help track AI performance over time and guide iterative improvements.

5. Address Edge Cases and Ambiguity

Some responses may fall into a gray area where classification is uncertain. To handle such cases:

• Refine classification prompts to provide clearer instructions.

• Introduce a confidence threshold, flagging borderline cases for manual review.

• Use multiple classifier runs, applying majority voting across multiple classification attempts for greater reliability.

Conclusion

Testing AI agents presents unique challenges due to their non-deterministic nature. Unlike traditional software, where a single input leads to a predictable output, AI systems—particularly those leveraging LLMs—produce variable responses influenced by model parameters, updates, and context. To ensure continuous improvement and reliability, AI testing must embrace automation, statistical methodologies, and a structured approach to evaluation.

A key strategy in AI testing is to decompose agent workflows into deterministic and non-deterministic components. By isolating deterministic elements, we can apply conventional unit tests, while statistical methods and classification models help validate non-deterministic outputs. This hybrid approach allows for scalable, consistent, and reproducible testing, ensuring that AI agents perform reliably across a range of inputs and conditions.

Furthermore, every change to an AI agent—whether it involves prompt modifications, model upgrades, or parameter adjustments—should be treated as a statistical experiment. By collecting baseline performance metrics, running controlled experiments, and analyzing results using statistical significance tests, we can make data-driven decisions that enhance AI effectiveness while minimizing unintended regressions.

Leveraging LLMs for classification transforms subjective AI evaluation into an automated, standardized process. Using AI to test AI enhances scalability and consistency while allowing teams to track performance trends over time. This method enables organizations to integrate AI testing into CI/CD pipelines, ensuring that model improvements align with business objectives.

Ultimately, robust AI testing frameworks not only enhance model performance but also instill confidence in AI-driven systems. By adopting systematic and automated testing strategies, we can build AI agents that are both powerful and dependable, paving the way for responsible AI deployment in real-world applications.

About the author
Guy Arieli, Co Founder and CTO – BlinqIO
Guy co-founded and CTO of Experitest (acquired by NASDAQ:TPG) and founded Aqua that was acquired by Matrix (TLV:MTRX). Prior to that Guy held leadership technology roles in startups & large traded companies including Atrica, Cisco, 3Com, HP – test automation engineer/lead. Guy holds a BsC Engineering – Technion and recently completed Machine learning courses in TAU.

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