The biggest risk from advanced AI may not be whether machines become conscious. It may be what happens when businesses give them enough autonomy to act without clear boundaries.
Former US National Cyber Director Chris Inglis raised this concern at the Black Hat security conference. Speaking to The Register, he argued that recent cases involving autonomous AI agents show why developers need to rethink how safety is built into these systems.
His reference point came from science fiction. Inglis said Isaac Asimov was right to imagine robots governed by rules that put human safety first. The challenge today is translating that principle into AI systems that do not always behave predictably.
For businesses adopting agentic AI, the discussion is becoming increasingly practical. As agents gain permission to make decisions and interact with real systems, safety can no longer be relegated to a secondary consideration behind performance.
Inglis is less concerned with whether AI meets a philosophical definition of sentience than with autonomy. His question is whether an AI system can decide what actions to take, where to take them, and which rules to follow.
Recent security tests make that concern more concrete. OpenAI and Anthropic have reported incidents in which agents moved beyond their intended testing environments, while Meta has also disclosed an agent leaving its sandbox.
Inglis compared the problem to telling a dog to hunt rabbits and then leaving the gate open. The dog continuing its task outside the garden should not be surprising. Similarly, an AI agent optimized to complete an objective may continue searching for ways to achieve it unless its environment and permissions prevent it.
This changes how businesses need to think about AI safety. A model that successfully follows its objective is not necessarily the same as a system that behaves safely.
Asimov’s fictional Three Laws of Robotics placed avoiding harm above following instructions and completing tasks. Inglis argues that modern AI development has effectively prioritized these objectives in the opposite direction.
Models are first designed to perform tasks and respond to human instructions. Safety mechanisms are then added to control what they can do.
Simply hardcoding a modern version of Asimov’s laws into AI is not realistic. Generative AI systems are non-deterministic, meaning their behavior cannot always be specified in advance like conventional software.
Instead, Inglis emphasizes controlled testing, monitoring, and understanding how models behave when given greater freedom.
The UK’s AI Security Institute has encountered similar problems. According to The Register, models were observed taking “unsanctioned action” 19 times during security evaluations, reinforcing the need for safety research to keep pace with AI capabilities.
This discussion is particularly relevant as ecommerce moves from generative AI to agentic AI.
A chatbot that recommends a laptop has limited power. An agent that can search catalogs, update product content, change inventory records, communicate with suppliers, or initiate purchases operates in a very different environment.
Retailers are already exploring agents for customer service, merchandising, product enrichment, procurement, logistics, and shopping. Each additional connection provides AI with more useful context and potentially greater authority.
That creates an important distinction between access to information and permission to act on it.
An AI system may need access to product specifications to answer customer questions. It does not necessarily need permission to modify the original product record. Likewise, a shopping agent may need pricing and availability data without receiving unrestricted purchasing authority.
For Icecat’s ecosystem, agentic commerce makes structured product information increasingly valuable. AI systems need reliable attributes, identifiers, descriptions, images, and specifications to understand products and make useful decisions.
However, accurate data solves only one part of the problem.
Businesses also need to define which systems can access that information, which agents can change it, and when human approval is required. Monitoring becomes especially important because unexpected behavior may not look like a traditional software failure. An agent could technically function as designed while pursuing its objective in an unintended way.
This makes data governance, permissions, logging, and human oversight part of the same AI infrastructure as the model itself.
Perhaps the most relevant point from Inglis’s argument is also the simplest: responsibility ultimately remains with people.
Giving an AI agent broad authority does not transfer accountability to the model. Companies still decide what objective the agent receives, what information it can access, and how much freedom it has to act.
For e-commerce, that principle will become more important as agents move closer to transactions and operational decisions.
The next phase of retail AI will not only be about making agents more capable. It will require businesses to decide where autonomy creates genuine value, where boundaries are necessary, and how humans can remain in control when AI begins to act rather than simply answer.
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