Testing in Real-World Conditions

Lab testing isn't enough. Your agent needs to work with messy real-world data, handle edge cases you didn't anticipate, resist malicious users, and scale under load. Comprehensive real-world testing means simulating production conditions before launch—typical use, edge cases, adversarial attacks, and stress scenarios. If it works in the lab but fails in production, you didn't test enough.

✅ Test Coverage

Cover typical, edge, adversarial, and stress scenarios

🔍 Real Data

Use production-like data with actual user patterns

Interactive: Test Scenario Runner

Select a test scenario and run simulations to validate agent behavior:

Typical Use Cases

Common, expected inputs that users will frequently provide

Test Cases:

•Standard queries

•Normal data ranges

•Expected workflows

•Happy path scenarios

Expected Result:

Agent handles smoothly with high accuracy and good UX

Common Issues:

Overfitting to edge casesIgnoring common patterns

💡

Shadow Mode Testing

Before full deployment, run your agent in "shadow mode"—it processes real production traffic but doesn't affect users. Compare shadow agent outputs to the current system. This reveals real-world performance without risk. Only promote to full production after shadow mode proves reliability.

Introduction to Agent Evaluation

Your Progress

Testing in Real-World Conditions

✅ Test Coverage

🔍 Real Data

Interactive: Test Scenario Runner

Typical Use Cases