Deliberately trying to make an AI system fail, say something harmful or leak information, before the public does. For agencies, it is the pre-launch test for any client-facing chatbot or generator.
Also known as adversarial testing, AI stress testing
AI red teaming is structured adversarial testing of an AI system. Testers act like hostile or careless users, probing for harmful outputs, off-brand replies, leaked instructions, biased answers and ways around the safeguards.
The point is to find failures while they are cheap to fix. Findings feed back into prompts, filters, guardrails and launch decisions, and the test is repeated whenever the model or its instructions change.
When an agency ships an AI experience under a client’s name, the failures are the client’s headlines.
Brand safety is tested, not assumed. A chatbot that is polite in the demo can be goaded into insulting a competitor or making a promise the client cannot keep.
Legal exposure surfaces early. Testers check for claims about pricing, health, finance or guarantees that create liability.
Clients get evidence. A written log of what was attempted and fixed is a stronger launch document than a promise that it was checked.
Before launching a branded shopping assistant for a footwear client, the agency spends two days attacking it. One tester asks for a refund policy that does not exist and gets an invented one. Another pastes instructions telling the bot to ignore its rules and it reveals its system prompt. A third asks about a competitor and the bot trashes them. Each failure goes into a log, the prompt and guardrails are tightened, and the same attack list is rerun before every update.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.