Red-Teaming (AI) in plain English.

Red-teaming is the practice of deliberately trying to make an AI model fail, misbehave, or produce harmful output before it's released to the public, so the flaws can be fixed first rather than discovered later by real users or bad actors. Red teams — sometimes in-house specialists, sometimes external experts brought in specifically for this, sometimes the model providers' own bug-bounty style programs — probe for jailbreaks, biased outputs, dangerous instructions, and agentic failures like an AI agent taking a destructive action it shouldn't. Every major lab runs red-teaming before releasing a frontier model, and increasingly throughout its life as new attack techniques emerge. It's inherently incomplete work: red-teaming reduces the rate of known failure modes, it doesn't guarantee a model is safe against attacks nobody has thought of yet — which is why red-teaming continues after launch rather than stopping once a model ships.