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AI Dev 26 x SF | Andi Partovi: Why Every Agent Needs a Simulation Sandbox
DeepLearning.AI16 June 2026Watch on YouTube
Description
AI agents fail in unpredictable ways that traditional testing can't catch — hallucinations, wrong tool calls, policy violations, and more. Teams only discover these failures after users hit them in production. A simulation sandbox gives you a controlled environment with realistic users, tools, and workflows where you can run hundreds of scenarios against your agent before it ships, catching edge cases and adversarial inputs that would be impossible to test manually. This talk by Veris AI's Andi Partovi covers why simulation-driven development is becoming essential infrastructure for any team building production AI agents, and how it closes the gap between "works in demos" and "works at scale."
What you'll learn
- AI agents fail in unpredictable ways (hallucinations, incorrect tool calls, policy violations) that traditional testing cannot catch
- A simulation sandbox provides a controlled environment with realistic users, tools, and workflows to test hundreds of scenarios before production
- Automated scenario testing catches edge cases and adversarial inputs that would be impossible to find through manual testing
- Simulation-driven development bridges the gap between demos that work and systems that scale reliably in production
Frequently asked questions
What types of errors do AI agents make that traditional testing cannot catch?
Hallucinations (making up information), incorrect tool usage, policy violations, and other unpredictable behaviors that only surface when users encounter them in production.
How does a simulation sandbox work for testing AI agents?
A simulation sandbox is a controlled environment with realistic users, tools, and workflows where you can run hundreds of scenarios against your agent before deployment, capturing edge cases and adversarial inputs that would be impossible to test manually.
Why is simulation-driven development critical for production AI agents?
Simulation-driven development is essential infrastructure because it bridges the gap between demos that work and systems that actually scale reliably in production environments.
What are the benefits of automated scenario testing for AI agents?
Automated testing catches unexpected behaviors before they reach users, enables testing of edge cases and adversarial inputs that would be impossible manually, and allows errors to be found in a safe environment.