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Welcome to the Age of AI Agents
One of the most important categories in Artificial Intelligence today is the AI Agent. These agents are not a single component of the AI world but an amalgamation of several technologies working in concert. They integrate natural language processing, automation, decision-making, and machine learning and are built to mimic human capabilities, perform tasks in software, and help solve complex problems. They are more than just a software—they are virtual assistants capable of learning, adapting, and performing multiple tasks autonomously.
The rise of AI agents didn’t happen overnight. The initial concept can be traced back to expert systems in the 1980s, where computers were programmed to mimic human decision-making for specific tasks. But it was the rise of machine learning and automation in the 2010s that led to a breakthrough. AI agents as we know them today emerged as different AI technologies were layered together, creating highly adaptive systems that could handle varied tasks seamlessly. The appeal for businesses? Reduced labor costs, increased productivity, and minimized human error.
Popular chatbots like ChatGPT and Gemini are great at using natural language to answer questions and write content, but they have many limitations in an enterprise business context. The first is these models are trained on general cases, not on the specifics of a company’s inner-workings. If a company wanted to upload their financials, for example, into ChatGPT to glean insights, it would require sharing that data with Open AI. The second issue is that these chatbots cannot execute tasks. If one were to ask ChatGPT to create a job in a third party application, it would fail to do so. The AI Agent solves both of these problems– not only can an AI Agent execute tasks in third party applications, they are also trained specifically for a company and do not share data for other users to interact with.
AI agents have become more appealing to businesses over the last few years primarily due to advancements in computational power and the availability of vast datasets. As more companies enter the agentic AI space, competition has driven rapid innovation. AI agents today aren’t just good at automating repetitive tasks—they can engage in decision-making, predict outcomes, and flag anomalies through the course of a workflow. Businesses now have access to AI that can learn from its environment, making these agents indispensable in their quest to streamline operations.
How AI Agents Impact Your Software
What do these AI Agents look like? How does someone interact with them? Think of AI Agents as living “on top” of all your existing software applications. They orchestrate all of the workflows and tasks that happen across your business daily. In AI agent software, you can give agents commands and track the tasks they are executing. It’s deployed through the cloud or on-premise, and while it may look similar to traditional software, AI agent software is unlike any software you may have used before
For decades, form-based applications have been the standard for businesses. These applications are essentially digital filing cabinets and require human operators to manually input data into digital forms— a modernization of the traditional paperwork processes. The evolution of form-based software brought efficiency gains compared to pen-and-paper systems but still relied heavily on human interaction. Think of early spreadsheets, accounting systems, and project management applications– they may allow your teams to share information quickly, but they still require humans to gather data, fill out forms, and export data manually. The software was essentially a tool; it empowered the worker but didn’t replace any of the work itself.
The AI agent concept flips this world on its head. AI agent software is no longer just a tool in the hands of the user, but rather an autonomous worker. Software is no longer the tool for organizing work– software becomes the worker itself. AI agents go beyond static inputs and structured fields; they learn from unstructured data, interpret context, and make decisions. Instead of requiring a person to fill in each piece of information, these agents can proactively scrape, analyze, and complete the tasks themselves.
This transition may feel daunting, but the long-term benefits are unmistakable. Efficiency gains, error reduction, and accelerated project timelines are just the beginning. AI Agents’ ability to learn and adapt to specific workflows means they’re not only efficient but also increasingly effective over time. For critical industries like construction, manufacturing, and energy, unlocking this efficiency is crucial.
