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How AI Agents Are Transforming IoT: From Smart Systems to Smarter Operations 

Joydeep Misra
Popularity of “AI agents” on Google Trends (trends.google.com)

The Internet of Things (IoT) has come a long way in helping businesses automate processes, monitor operations, and make better decisions with real-time data. From connected machines on the factory floor to smart grids and HVAC systems, IoT platforms are already helping organizations become more efficient and responsive.

But as devices proliferate and operations grow more complex, the question arises: how can these already “smart” systems become smarter?

Here is where AI agents—powered by Generative AI—come into play.

In this article, we’ll explore what AI agents are, how Generative AI enables them, and why they are emerging as a natural evolution of IoT systems. We’ll also look at how real-world applications are using AI agents to move from simple automation to intelligent action.

Why Did AI Agents Become so Popular?

Until recently, the term “AI agents” was mostly used in academic papers or research circles. That changed in mid-2024.

Check out this graph:

Popularity of “AI agents” on Google Trends (trends.google.com)

Search interest in “AI agents” stayed relatively flat for years, until it suddenly spiked. This surge wasn’t random. It was fueled by a convergence of technological breakthroughs, especially in large language models (LLMs).

When models like GPT-4, Claude, and Gemini began generating human-like responses with remarkable accuracy, strategists and engineers alike began to ask,

‘If AI can understand and respond like a human, why not allow it to take action too? ‘

That idea sparked the modern wave of AI agents. But LLMs alone weren’t enough. Several other innovations turned this concept into a practical and scalable reality:

Agents now learn by doing—improving through feedback and trial and error. This allows them to fine-tune decisions in real-world environments and become more reliable over time.

Instead of relying on a single AI, systems now leverage multiple AI agents that collaborate like human teams—sharing information, breaking down tasks, and solving problems in parallel.

Modern AI agents aren’t confined to text. They can interact with databases, control devices, send emails, call APIs, and execute real-world actions—bridging the gap between intelligence and automation.

Earlier models lacked memory, making long-term interactions clunky. Today’s agents retain context and recall previous conversations or actions, which is crucial for persistent workflows.

Together, these advancements have transformed AI agents from a theoretical buzzword into a practical, industry-shaping tool, especially in areas like IoT, where action and insight need to go hand in hand.

What Is an AI Agent?

At its core, an AI agent is a software entity capable of perceiving its environment, processing information, and taking actions to achieve a goal. Unlike traditional automation tools that follow static rules, AI agents are built to be adaptive, context-aware, and interactive.

In IoT environments, this means going beyond dashboards and alerts. AI agents:

Think of them as digital assistants embedded in your operational workflows—capable not only of understanding what’s happening but also of knowing what to do next.

Generative AI: The Engine Behind Modern AI Agents

Generative AI refers to models that can create new content such as text, images, code, or decisions, based on large volumes of training data. Unlike traditional AI models, which classify or predict based on fixed inputs, generative models can synthesize responses to a wide range of inputs, even those they haven’t seen before.

When applied to AI agents, Generative AI enables:

This flexibility makes AI agents ideal for real-world environments where issues are rarely black and white.

Why AI Agents Matter for IoT

While IoT platforms have made devices “smart” by enabling connectivity and automation, many systems still rely heavily on human intervention, especially when interpreting complex data or responding to issues.

AI agents fill this gap by turning smart systems into decision-making systems. They add a layer of cognitive intelligence that helps interpret the data, prioritize actions, and close the loop with minimal manual input.

Here are a few reasons why AI agents are becoming increasingly valuable in IoT use cases:

IoT dashboards are great at visualizing data, but someone still needs to interpret it. AI agents can detect anomalies, diagnose causes, and recommend or execute the next steps automatically.

Not every location has access to domain experts. An AI agent trained on equipment manuals, historical performance data, and operating procedures can serve as a 24/7 advisor—even for junior technicians in the field.

This is especially critical in Industrial IoT (IIoT) scenarios, where remote monitoring and expertise are vital.

AI agents help reduce downtime by quickly narrowing down the problem and presenting context-aware suggestions or initiating a remote fix where possible. With edge computing, these agents can process data locally, allowing for faster response times and reduced latency in critical operations.

By identifying early warning signs from sensor data, AI agents can recommend maintenance before a failure occurs, supporting a shift from reactive to predictive operations.

From Smart to Smarter: The Next Evolution in IoT

So, what does “smarter” really mean in the IoT world?

Here’s a simple way to think about it:

Smart Systems

Smarter Systems (with AI Agents)

Send alerts when thresholds are crossed

Analyze and explain why the threshold was crossed

Offer dashboards and logs

Provide conversational summaries and actionable insights

Require manual interpretation and intervention

Offer step-by-step guidance or trigger actions automatically

Operate on static rules

Continuously learn and adapt from new data

 

In other words, smart systems inform. Smarter systems interpret and act.

A Real-World Example: Jera from the Interscope AI Platform

One practical implementation of an AI agent can be found in Jera, a solution developed as part of Bridgera’s Interscope AI Platform. Jera is designed to assist industrial and IoT customers with intelligent operational support by leveraging customer-specific data such as machine datasheets, user manuals, and historical performance logs.

Rather than offering generalized responses, Jera is trained in the context of each deployment. This allows it to provide highly relevant assistance across a range of use cases, including:

By embedding Jera directly into the IoT workflow, organizations can reduce downtime, streamline decision-making, and scale domain knowledge across teams, especially in distributed or resource-constrained environments such as smart factories.

Jera illustrates how AI agents are no longer experimental. They are already being applied in production settings to bridge the gap between human decision-making and machine automation.

Looking Ahead

AI agents represent a significant step forward in how we interact with connected systems. They make IoT not just about visibility but about intelligence, turning real-time data into real-time decisions.

As Generative AI models become more advanced and easier to integrate, we will likely see more AI agents embedded in everyday industrial and operational environments.

Whether you’re managing a smart facility or a fleet of connected devices, moving from smart to smarter is no longer just an aspiration—it’s becoming a practical next step. Ready to connect your devices and see how AI Agents can power your innovation? Interscope AI is now available for free, for your first 5 assets or devices.

About the Author:

Joydeep Misra, SVP of Technology

Joydeep Misra is a technologist and innovation strategist passionate about turning complex data into simple, actionable intelligence. At Bridgera, he leads initiatives that blend IoT, AI, and real-world operations to help businesses move from connected to truly autonomous systems. With over a decade of experience in building enterprise-grade platforms, Joydeep is a strong advocate for practical AI adoption and believes that the future belongs to those who can make machines think and act.

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