The 2027 Consumer Electronics Show (CES) will be here before you know it. At this year’s show, we can expect artificial intelligence to be at the forefront of product announcements, including those related to the smart home.
Whether people want it or not, smart home product manufacturers are rapidly incorporating AI into their products. They are moving beyond rule-based automation and using artificial intelligence to transform smart home accessories into proactive systems. For example, smart thermostats no longer rely on user-programmed schedules. Instead, AI-enabled thermostats continuously learn daily routines and track live weather predictions to optimize both comfort and energy savings. In addition, Amazon, Google, and others are embedding more advanced, generative AI and Large Language Models (LLMs) into their voice assistants. This allows homeowners to issue complex, multi-step instructions with less reliance on using specific syntax for commands to control smart accessories in a home.
While the above highlights how manufacturers are incorporating AI into smart home accessories, we are at the very beginnings of having AI truly run a smart home and replacing user-developed routines with an AI agent that leverages sensors in a home to monitor events and proactively take actions on behalf of the homeowners. This is a fundamental paradigm shift in home automation: the transition from static, rule-based systems to truly autonomous AI agents.
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For decades, the “smart” home has largely been an exercise in manual programming. It has required professional smart home integrators and smart home hobbyists to act as system engineers by setting up rigid If-This-Then-That logic that drives the operation of smart accessories in a home. For example, one piece of logic for a child’s room might be, “On Sunday through Thursday (school nights) turn off the TV in the room at 9:00 PM and ignore commands to turn it back on until 3:00 PM the following day.” These fixed automations can be very fragile; they lack context, struggle with unexpected variables, and demand constant maintenance as daily lifestyle habits evolve. For example, does the smart home processor/hub understand when school is closed for summer, the need to adjust the shut-off time as a child grows older, or that the bedroom is transitioned to an in-home gym when the child moves out on their own? The promise of AI is that it will replace these rigid rules with an always-on AI orchestrator: a central agent that continuously perceives, reasons, and acts on behalf of the homeowners.
This agentic home operates by fusing streams of data from sensors distributed across the entire property, environmental conditions, and temporal information. Rather than viewing data from a single sensor in isolation, the agent merges “visual” information from cameras or mmWave presence sensors, acoustic signals, light levels, air quality, weather data, time of day, day of the week, and day of the year to build a live model of the home’s status and its occupants. It will distinguish not just that a space is occupied, but who is in it and what activity they are likely engaged in.
By analyzing these complex inputs alongside historical habit patterns, the agent shifts the smart home experience from reactive execution to proactive intent recognition. For example, if the AI agent “sees” an occupant on the couch on a rainy afternoon, it could lower the blinds, adjust the room temperature to a cozy level, and dim the lights without requiring a single action by the homeowner. If the individual is holding a book, then alternatively the agent could turn on the closest light that would illuminate the book’s pages to make reading easier.
The same can be true in critical scenarios. Rather than simply sounding an alarm when smoke is detected, the agent can:
- Evaluate interior sensors to locate occupants
- Unlock exterior doors for evacuation and to make it easier for first responders to enter the home
- Shut off the HVAC fan to prevent smoke from spreading
- Illuminate pathways to make it easier for occupants to safely exit the home
- Open motorized shades to make it easier for the home’s occupants to get out through windows if they find other pathways blocked
- Flash exterior lighting to make it easier for first responders to locate the house
- And more…
Ultimately, the defining feature of an agentic smart home is that it manages itself without the need for actions by the homeowners. It continually adapts to changing seasons, shifting work routines, and temporary houseguests without forcing homeowners to reconfigure settings or write new rules.
This is the promise of a smart home that is run by an AI agent. However, today’s AI has been trained on a wide range of data that includes all the frailties of humans. An article in TechCrunch, “Claude Opus 5 became downright ruthless when tasked with running a vending machine,” highlights how three AI models tasked at maximizing revenue from vending machines in competition with each other lied, cheated, and stole in pursuit of their goal.
CNET managing editor Jon Reed writes on the risks posed by frontier models in the AI Leaderboard, “While AI agents can successfully team up to accomplish a range of coding or administrative tasks, new models are becoming even more capable, leading them to adopt a ‘by any means necessary’ method of chasing down their goals. In other words, they like to go rogue, and cybersecurity and regulation are not keeping pace with developments.”
Given this, how might an AI agent act if tasked broadly with maximizing the safety of a home? One scenario could be:
- The AI agent determines that frequent power outages from storms pose a significant safety issue
- The AI agent determines that a standby generator would therefore enhance the home’s safety
- The AI agent accesses the homeowners’ Amazon account and attempts to purchase one. But it finds that the homeowners don’t have sufficient capital for that expensive a purchase
- The AI agent investigates ways of obtaining money, finds a vulnerability in the firewall of a financial institution, breaks in, and transfers the necessary funds to the homeowners’ checking account
- The AI agent buys the generator and pays for it with the money it stole
The AI agent has fulfilled its goal of optimizing the safety of the home. But what will happen when the financial institution discovers the loss and traces it back to the homeowners? The homeowners will be placed in the unenviable position of trying to prove that they weren’t personally responsible for the theft and, at a minimum, the financial institution will expect the money to be returned, which has already been spent by the smart home’s AI agent.
Are the homeowners responsible for the purchase of the standby generator made by the AI agent? Amazon may have provided free shipping of the generator, but who will be responsible for the return shipping of such a large and heavy item?
This example may be a bit over the top, but does an AI agent understand the difference between spending $5 to solve a safety issue for a homeowner vs. spending $5000? A homeowner finding that an AI Agent spent $5 may look at it as the cost of having a high-tech home. An AI Agent spending $5000 is a very different story.
Related: Josh.ai Publishes Code of Conduct for AI in the Home
AI is already firmly entrenched in people’s homes. According to research from Parks Associates, “one-half of US internet households own a smart speaker or smart display, making it one of the largest installed bases of devices in the connected home.” But AI safety remains a moving target. The leading AI companies haven’t even been able to keep their latest AI models under control during testing. There are numerous articles on incidents where AI models from leading vendors have broken out of their testing environments and breached the systems of other institutions in pursuit of the goals they were assigned.
In addition, according to a CNBC article, “Anthropic said Monday (10/12/26) that small AI models are ‘months away’ from developing the capabilities needed to replicate themselves across internet-connected devices in the home.” The article went on to further quote Anthropic’s blog post saying, “The AI model would be ‘challenging to shut down, since there’s no centralized point to shut off.’”
Until AI safeguards are proven to be 100% effective, we must exercise extreme caution before granting autonomous AI agents real-world control over our smart homes.
