Most teams think they “just use AI tools.” Regulators don’t care who trained the model. They care that your AI touches patients, money, or rights.

If you plug an AI receptionist into a clinic, let an AI agent nudge credit decisions, or route privileged calls through an AI assistant at a law firm, you’re not a casual user anymore—you’re a deployer.

In this episode, Maya breaks down the difference between using and deploying AI agents in regulated workflows, the “last Tuesday” test for whether you’re really in control, and the four boring obligations that come with deployment:

• Oversight – someone owns what the agent is allowed to do

• Logs – you can answer “what did it do last Tuesday?” with specifics

• Explainability – you can show the reasons behind decisions that affect real people

• Boundaries – hard limits on what data the agent can see and when it must escalate

If your AI agent touches care, credit, or privileged client information, you’re already a deployer. Better to build the oversight, logs, and boundaries on purpose than pretend you’re “just using AI” until someone with authority disagrees.

Keywords: AI agents, agentic AI, AI deployer, AI compliance, AI regulation, EU AI Act, AI receptionist, healthcare AI, fintech, legaltech, AI governance, AI orchestration, AI infrastructure, CTO, VP Engineering

This is Maya. New episodes three times a week.
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