When machines talk back: The rise of agentic AI in a world of physical devices

A shift from traditional machine-to-machine (M2M) communication to agent-to-agent (A2A) interaction represents a revolutionary leap, enabling devices to learn, adapt and collaborate in real-time across varied domains like industrial automation, healthcare and urban infrastructure.

For decades, machine-to-machine (M2M) communication has quietly powered the infrastructure of our digital world. From automated meter readings to fleet management systems, these interactions have been primarily rule-based: one machine detects a status and sends a message to another, often triggering a predetermined response. These systems have been foundational in logistics, utilities, and industrial automation, delivering speed and consistency.

But what happens when the machines involved are no longer just communicating—they are thinking, deciding, and negotiating? As digital complexity scales, static scripts and centralized control architectures often fall short. Enter agentic AI.

Agentic AI vs. Generative AI

To baseline, let’s remember that agentic AI is quite different from generative AI tools that have dominated recent headlines. Generative AI (like ChatGPT and DALL-E) creates new content from massive databases populated with a wide range of different kinds of data. And frequently that data is “old”, with even the best tools still relying on training from data >1 year ago. Retrieval augmented generation (RAG) is getting better, allowing generative AI tools to scan the web for more recent data, but the tools are better for legacy research and creation than real-time engagement. Generative AI excels at pattern recognition, synthesis, and expression—from writing stories to generating business plans or producing realistic audio and visuals.

Agentic AI, on the other hand, is about action. AI agents are trained on extremely narrow and deep, domain-specific data sets and often tied to real-time sources of data like IoT data streams and dynamic database API’s. An agentic system senses its environment, makes autonomous decisions, adapts its behavior, and operates toward a goal. Unlike generative AI, which outputs content, agentic AI outputs decisions and actions.

Let’s look at some examples of how agentic AI can be embedded into physical systems.

Industrial automation: Imagine a warehouse robot that not only picks products but decides when to recharge, avoids high-traffic zones based on real-time updates, and coordinates with other robots to balance workload. If a new shipment is delayed, the agents update their plan and prioritize other tasks. This is not scripted automation—this is a hardware system with agency.

Smart energy: Consider a smart HVAC unit that doesn't just respond to a thermostat, but negotiates energy use with other appliances in a home based on real-time electricity prices, personal preferences, and weather forecasts. If a major storm is forecast, the HVAC might collaborate with a solar battery system to store extra power in advance.

Supply chain logistics: In supply chain management, agentic systems can negotiate pricing and timing with each other across companies’ platforms. An AI-driven shipping container may decide to reroute itself if it detects a bottleneck at the originally planned port. And once in port, container cranes, autonomous trucks, and dock scheduling software now operate with AI agents. When a ship docks early, agents communicate in real time to shuffle unloading schedules, reroute trucks, and reduce idle time. Each agent understands both its local constraints and the broader system goals. The result is reduced fuel usage, higher throughput, and increased resilience against last-minute changes.

Agriculture: In precision agriculture, drone fleets equipped with agentic software can work collaboratively. One drone might detect high weed density and alert others to increase pesticide application in that area. Meanwhile, soil sensors negotiate irrigation adjustments with the drones based on moisture levels and upcoming weather. This eliminates the need for constant human oversight, allowing farmers to focus on broader resource planning.

The shift to agent-to-agent communication

With agentic AI embedded in devices, communication becomes semantic and context-driven. Agents aren't just exchanging sensor data; they're negotiating plans, adapting priorities, and collaborating across domains.

It is important to note that AI agents are ideal for edge applications. In the agriculture example above, the soil sensors do not need to have the processing power and long-range connectivity to analyze weather reports directly. They can have intelligence that monitors local soil conditions and waters based on those isolated measurements, in absence of other data. But when a drone comes by to share additional intelligence, the agent can now change from its original rule-based approach to a smarter system level decision – all without a high demand on processing or connectivity - which keeps the device simple and low cost.

So how are hardware embedded AI agents actually deployed today?

Real-world applications and implications

Manufacturing: Self-healing production lines

In smart factories, equipment embedded with agentic AI can detect potential failure before it happens and autonomously shift workflows to alternate machines. The goal isn't just predictive maintenance—it's resilient operations where machines actively collaborate to keep the line running. The biggest cost to a manufacturing facility is downtime. Predictive maintenance was the best that old M2M techniques could achieve. Embedded AI agents take us to the next level. Human operators can supervise dozens of processes without needing to intervene in most issues.

Healthcare: Patient-centric agents

Wearable monitors like continuous glucose sensors are being paired with insulin pumps that can automatically adjust dosing. But more importantly, agentic systems can now integrate exercise data, diet patterns, and patient behavior to make dynamic care adjustments. In a recent clinical trial, patients using agentic closed-loop systems saw a more than 11% improvement of glycemic control (Time in Range, or the time patients managed to keep blood-sugar at the proper level) over those using manual devices. And as an unexpected side-benefit, patients saw an average 3.3 lb weight loss over the first month of AI automated support. As healthcare shifts toward personalized models, agents will play a crucial role in dynamic therapy and diagnostics.

Urban Infrastructure: Smart streets that adapt

In a pilot program in Helsinki, traffic lights, electric buses, and street cameras operate as agents on a shared protocol. If pedestrian density increases in one area, traffic signals coordinate to prioritize foot traffic, while buses adjust routes to alleviate congestion. During emergencies, agentic traffic systems can create rapid-response corridors for first responders without requiring centralized override.

Energy: Autonomous microgrids

In emerging microgrid projects, smart homes with solar panels and batteries act as agents that trade electricity with neighbors or back into the grid. During peak hours, homes can reduce load collectively. When power lines go down, these homes can isolate and operate in peer-to-peer mode, autonomously maintaining power within the community.

Are we ready to trust agentic AI hardware?

This MA2MA future is not without hurdles. Interoperability between different manufacturers’ agents remains a significant technical challenge. Without shared ontologies and communication protocols, agents may talk past each other—or worse, make conflicting decisions. The development of universal agent languages and agent-to-agent APIs is a growing area of focus.

Security is another concern. With agents making autonomous decisions, a compromised agent could have outsized influence. Who certifies the behavior of agents? How do we define acceptable ranges of action? Can we detect if an agent is acting maliciously or incorrectly before damage is done?

Ethically, the move toward machine agency forces us to revisit accountability. If a self-driving delivery bot reroutes to avoid danger and causes a delay, who is responsible? The designer? The owner? The agent? These questions will require updates to legal and insurance frameworks.

There is also the question of unintended consequences. Agents that are rewarded for efficiency might ignore human-centric considerations like fairness, accessibility, or long-term risk unless explicitly coded to account for them. I think this may be the biggest risk, as we look towards the future.

The “industry of business” has always been predisposed towards maximizing profit. And the typical way that emerging technologies enter industry is first through creation of efficiency gains. If we program agents with heavy algorithmic weighting towards efficiency, we miss the opportunity for other kinds of value creation to emerge.

How should the industry proceed?

As agentic AI becomes increasingly common in physical devices, we must consider how to shape this future responsibly. A few key focus areas include:

● Standardization: Initiatives like the IEEE P7000 series are beginning to define ethical and functional standards for autonomous systems. These frameworks help designers embed values into their agents early in the development process. [Aside – I will take a deep dive into this series of standards in next week’s article].

● Policy: Local governments and national regulators will need frameworks that treat agentic systems as semi-autonomous actors. In many ways, the policy discussion will mirror the evolution of cyber policy—just with more unpredictable actors.

● Design: Entrepreneurs and engineers must think not just about functionality, but about negotiation, cooperation, and alignment of values across agent networks. Design tools must evolve to allow simulation of agentic interactions before deployment. There will be a huge intersection here with digital twin technologies, and areas that are further ahead in development of digital twin models (manufacturing, smart cities) may have an early-mover advantage here.

● Education: A new generation of technologists must be trained not just in machine learning, but in multi-agent coordination, ethics, and socio-technical systems. This is a huge risk area. We have never seen the tech sector to proactively consider adding behavioral scientists, anthropologists or other experts in the humanities to their design teams. The closest hires are UI/UX (user interface / user experience) experts, who tend to focus on the efficiency metrics I described above. As we design technology tools that are to make decisions like humans, we must have experts in human behavior on the early product team.

The transition from machines that follow commands to machines that form strategies represents a profound shift in how we interact with technology. This new world of agentic AI won’t just automate—it will negotiate, adapt, and in many cases, surprise us.

As we venture further into this world, we must prepare for new forms of digital negotiation, cooperation, and even competition. And in the spirit of agentic AI, the question isn’t just what machines will do for us. It’s what they will choose to do—with us, and with each other.