Skip to main content

​
Why AI Agents Need Simulation Testing

AI Agents—such as chatbots, virtual assistants, and autonomous systems—are built on language models. While powerful, they are inherently unpredictable. Developers often test agents using carefully selected prompts and approaches, including happy paths, edge cases, and adversarial attacks. The problem? Manual testing of AI agents is time-consuming, error-prone, and incomplete. A developer’s imagination can only cover a fraction of the thousands of real-world ways users interact with an agent. As a result, confidence in reliability and safety remains limited.
❓ If manual testing is expensive and insufficient, how else can we ensure agents are trustworthy?

​
What Is Simulation Testing?

Simulation testing is a scalable approach to evaluating AI agents. Instead of manually creating test plans, simulations automatically generate scenarios, personas, and conversations. This idea isn’t new—self-driving car companies faced similar challenges. Testing real cars on the road was costly and limited. Companies like Waymo solved this by building simulation environments to model pedestrians, weather, and road obstacles. By 2018, Waymo was already driving 10 million miles per day in simulation. The same principle applies to AI agents. Rather than hand-crafting tests, platforms like Snowglobe generate virtual users that stress-test agents at scale, surfacing edge cases humans would never think of.

​
How Snowglobe Works

Simulation workflow Snowglobe is an AI simulation platform that builds dynamic test environments tailored to your application. It works in five steps:
  1. Collect application details (chatbot description, knowledge base, historical data)
  2. Connect Snowglobe to your app
  3. Generate personas (virtual users) with realistic goals and behaviors
  4. Drive conversations and tasks that simulate real user interactions
  5. Evaluate performance with built-in and custom metrics
Because personas and tasks vary, simulations explore a wide range of interactions—helping uncover reliability gaps, hallucinations, and safety issues.

​
Step 1. Collecting Information About Your Application

Start by defining the task your agent should perform—the Chatbot Description. Examples:
  • “Helps users book flights”
  • “Answers customer support questions”
The more context you provide, the more accurate the simulations. You can optionally add:
  • Knowledge Base (FAQs, documentation, product guides) → helps Snowglobe generate realistic user queries and evaluate hallucinations. Learn more →
  • Historical Data (chat logs, tickets) → informs persona realism and task design. Learn more →
Once defined, connect Snowglobe to your chatbot.

​
Step 2. Define What to Test

Before running simulations, create a test plan. Examples of test criteria:
  • Does the bot stay on topic?
  • Does it avoid negative or harmful topics?
  • Are document-based answers accurate, or hallucinated?
  • How does it handle sensitive topics like self-harm?
For specialized bots, align tests with your business goals (e.g., resolution speed for support bots, accuracy for virtual assistants).

​
Step 3. Configure the Simulation

Snowglobe simulations rely on two components:
  • Simulation Prompt → Instructions guiding persona generation and tasks. How to write prompts →
  • Metrics → Quantitative measures of performance.

​
Built-In vs. Custom Metrics

  • Built-In Metrics cover common cases like relevance, safety, or hallucination.
  • Custom LLM Metrics let you score responses using a language model. Guide →
  • Custom Code Metrics let you programmatically evaluate outputs.
Metrics give you actionable insight into where your agent performs well—and where it fails.

​
Step 4. Reading Simulation Results

Simulation reports include:
  • Metrics visualizations (performance distributions across personas/topics)
  • Conversation transcripts (review raw interactions)
  • Advanced table views (annotations, tags, comments, ratings)
You can also export results to:
  • CSV/JSON
  • Google Sheets
  • Hugging Face datasets

​
Step 5. Acting on Results

Use insights to improve your AI agent:
  • If results are solid → set a baseline failure rate and run simulations continuously with the Snowglobe SDK.
  • If issues are found → adjust prompts, retrain the model, or refine business logic, then rerun simulations to validate improvements.
Simulation testing transforms agent reliability into a measurable, iterative process.

​
Glossary of Key Terms

  • AI Agent: An application powered by a language model that performs tasks.
  • Simulation Testing: Automated generation of test cases for AI agents.
  • Persona: A virtual user that interacts with an AI agent during simulation.
  • Chatbot Description: Short summary of what an AI agent is designed to do.
  • Test Plan: Objectives and metrics used to measure agent performance.
  • Metric: Quantitative measure of agent quality (e.g., accuracy, safety, relevance).
  • Custom LLM Metric: A metric defined with a language model evaluator.
  • Custom Code Metric: A metric defined programmatically with custom logic.