FlakeStorm: Chaos Engineering for AI Agent Testing
Published:Jan 3, 2026 06:42
•1 min read
•r/MachineLearning
Analysis
The article introduces FlakeStorm, an open-source testing engine designed to improve the robustness of AI agents. It highlights the limitations of current testing methods, which primarily focus on deterministic correctness, and proposes a chaos engineering approach to address non-deterministic behavior, system-level failures, adversarial inputs, and edge cases. The technical approach involves generating semantic mutations across various categories to test the agent's resilience. The article effectively identifies a gap in current AI agent testing and proposes a novel solution.
Key Takeaways
- •FlakeStorm addresses a critical gap in AI agent testing by focusing on robustness under adversarial and edge case conditions.
- •It utilizes chaos engineering principles, treating agent testing like distributed systems testing.
- •The engine generates semantic mutations across various categories to test the agent's resilience.
Reference
“FlakeStorm takes a "golden prompt" (known good input) and generates semantic mutations across 8 categories: Paraphrase, Noise, Tone Shift, Prompt Injection.”