These agents may not roam the hallway, but they operate inside the enterprise trust boundary. They have credentials. They take actions. They make decisions. They access data. They call APIs. They can trigger workflows.

From a risk perspective, an autonomous AI agent is another actor inside your environment. It has the same three exposures as the robot. 

A vendor may be able to reach it. 
An attacker may be able to hijack it. 
And the system itself may do something no one intended.

The uncomfortable truth is this: a robot, an AI agent, or any autonomous connected system is not simply a product. It is continuously operating third-party operational technology living inside your trust boundary.

And right now, most organizations are not checking these systems with anything close to the rigor they apply to people, networks, applications, or traditional infrastructure.

That has to change.

Real validation needs to examine every layer 

At the hardware layer, enterprises need to understand what the machine physically is and what it can do. What sensors does it carry? Does it have cameras, microphones, lidar, radar, GPS, cellular radios, Wi-Fi, Bluetooth, USB ports, or local storage? What can it touch, move, lift, open, block, or damage? Can components be tampered with? Does the hardware include undocumented radios, debug ports, or maintenance interfaces? What is the blast radius if the machine malfunctions or is compromised?

At the software layer, organizations need to inspect the operating system, firmware, middleware, dependencies, update process, authentication model, access controls, logging, and vulnerability history. A robot fleet should not receive less scrutiny than an application server simply because it has wheels. The enterprise needs to know what software is running, who maintains it, how it is patched, how updates are signed, and whether the system can be independently tested.

At the communications layer, enterprises need to know exactly how the system connects and communicates. Is it using enterprise Wi-Fi, private cellular, public cellular, Bluetooth, satellite, or a combination? Where does traffic go? Which cloud services receive telemetry? Are control commands routed through the public internet? Is traffic encrypted end-to-end? Is a private tunnel required? Can the vendor remotely access the device? Can the command-and-control channel be isolated, monitored, restricted, or shut down?

At the command-and-control layer, the enterprise needs independent validation of who can issue commands, how those commands are authenticated, what actions are allowed, and whether command paths can be hijacked or abused. This layer matters because it is where digital compromise can become physical action. A weak command-and-control model can turn a useful robot fleet into a coordinated intrusion platform.

At the AI and behavior layer — the hardest and most neglected layer — organizations need to validate what the system actually does under real-world conditions. How does it behave when sensors are noisy? When instructions conflict? When adversarial inputs are introduced? When a user gives it an ambiguous command? When it reaches an edge case the vendor demo never covered? Does the system stay within approved boundaries, or can it be pushed into unsafe, biased, unauthorized, or operationally damaging behavior?

The industry needs a new discipline: independent IoT command-and-control validation 

Traditional security testing is necessary, but it is not enough. Compliance questionnaires are necessary, but they are not enough. Vendor promises are necessary, but they are not enough. Enterprises need a third-party validation service that can test autonomous and connected systems across hardware, software, communications, command-and-control, and AI behavior before they are admitted into production environments.