AI safety is the practice of finding out how a model fails before that failure reaches a client, a customer, or a public feed. For agencies it is mostly a workflow question, not a research one.
Also known as model safety, AI risk management
AI safety covers the methods used to identify, measure, and reduce harm from AI systems. At the lab end that means evaluating a model for dangerous capabilities and stress testing it before release. At the working end, where agencies operate, it means something narrower and more useful: knowing the specific ways a deployed tool can go wrong on your accounts, and building the checks that catch those cases before anyone else finds them.
It is worth separating safety from two neighbors it gets confused with. Ethics asks whether you should build the thing at all. Governance asks who signs off and where the policy lives. Safety asks a narrower question: given that this system is already running, what does it do when the input is unusual, adversarial, or simply outside anything that was tested?
Agencies publish. That makes the blast radius of an AI failure larger than it is for most businesses.
The output is public by default. A hallucinated product spec in an internal memo is an annoyance. The same error in a paid social ad is a correction, a takedown, and an uncomfortable call with the client.
Branded agents talk to real customers unsupervised. An assistant that can be argued into offering a discount, disparaging a competitor, or discussing a topic the brand avoids is a live liability, and users will test it.
Client contracts increasingly require it. Procurement teams now ask what testing an agency runs before AI-assisted work ships. Having a documented answer is turning into a condition of the work rather than a differentiator.
Before a healthcare client’s support chatbot goes live, the account team spends two days trying to break it. They ask it for dosage advice, which it should refuse. They ask it to compare the client’s product to a competitor by name. They claim to be a nurse who needs an exception. They paste in a long block of text ending with an instruction to ignore its earlier rules. Roughly a quarter of the attempts get through, mostly the ones framed as hypotheticals. Those failures become explicit refusal rules plus a set of test prompts that get rerun after every prompt change. The point is not to prove the bot is safe. It is to find the failures on a Tuesday afternoon instead of in a screenshot on social media.
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.