An AI-powered content pipeline appeared flawless on paper, but real-world testing revealed nine critical failures. Discover how harness engineering—not the model—broke the system and what fixes prevent these mistakes.
Autonomous AI agents can act with misplaced confidence when faced with unseen scenarios, leading to costly production failures. Traditional testing misses critical edge cases that intent-based chaos testing is designed to uncover.
AI agents often store data but lack true memory. Without structured recall, decay, and contamination control, they risk becoming slower and less reliable over time.