AI Agents 101: Unlocking the Future of Autonomous Automation

Dr. Andrew Ng, a leading mind in artificial intelligence, believes that the future of AI lies in Artificial Intelligence (AI) agents. In my years leading AI product development and innovation, I’ve witnessed firsthand how AI has evolved from a data processing tool to an adaptive partner in business. Generative AI and AI agents, in particular, will allow businesses to stay agile in ways that were unimaginable even a few years ago.

This article explores the fundamentals of AI agents, their potential impact, and how organizations can begin leveraging them to stay ahead in the AI age.?

What Are AI Agents?

AI agents are software systems capable of performing tasks without human intervention.?

There are various types of AI agents that can interact with both digital interfaces and physical objects. Here, we will primarily focus on AI agents for digital interfaces, but the concepts are applicable to other types of AI agents.

Unlike traditional automation systems, such as Robotic Process Automation (RPA), which follow predefined and rule-based scripts, AI agents can perceive their environment, process information, make decisions, learn from experiences, and take action to achieve specific goals.

They represent a significant advancement in automation, bringing intelligence and adaptability to tasks that were previously rigid and rule-based.

To better illustrate how AI agents achieve this level of intelligence and autonomy, let's look at the key components that enable their functionality.

• Perception: AI agents sense their environment using data inputs, much like humans use their senses. This sensing could involve processing text, images, voice, or other data forms to understand the context in which they operate.
• Decision Making: Equipped with algorithms and models, AI agents autonomously solve problems by evaluating options and selecting the best course of action based on their objectives.
• Learning: Through various machine learning techniques, AI agents adapt and improve over time. They learn from past experiences, data patterns, and feedback to enhance their performance. The learning capability is enhanced to be effective with a few examples, which significantly improved the agility of AI agents.
• Actuation: AI agents can actuate the decisions or instructions generated from the decision-making process. The actions can range from clicking a send button to filling out a form based on a PDF file. Actuation is a critical aspect of AI as it enables agents to interact with the real world and carry out their intended functions accurately and efficiently.

Together, these components enable AI agents to operate autonomously, learning and evolving with each interaction. By harnessing these capabilities, AI agents transform processes, allowing for smarter, more adaptable automation across digital environments.

How Do AI Agents Work?

AI agents operate as complex, multi-layered systems designed to perform tasks autonomously within a defined environment. They don’t just rely on a single model; instead, they incorporate various specialized components to interpret, interact, and respond within their environment.?

In a nutshell, AI agents work by repeatedly cycling through a sequence of observation, interpretation, action, and adaptation. This systematic, multi-component approach enables them to perform specific, complex tasks autonomously, efficiently, and with contextual awareness, often across varied environments and workflows. Each component in the stack has a distinct role, ensuring that the AI agent can gather and interpret data, make informed decisions, and act effectively within its designed context.

AI agents operate in a “relatively known environment”—a structured and somewhat predictable space, like a web browser, an operating system, a specific application, or a combination of the above. These environments are usually well-designed and comprehensible with common knowledge. This is critical because today's AI foundation models are mostly trained with common knowledge data that are available on the web. This familiarity helps AI agents make informed decisions and interpret the environment’s data accurately.?

The agent starts by gathering data from the environment. Using an observation module, it collects relevant details, converting them into a form it can process. This could include structured information like API interaction logs or unstructured data such as text or screenshots. This observation process is tailored to the agent’s specific needs and environment. For example, in a web browser, both the screenshot and the underlying HTML are observed and processed.

The collected data is then passed to a prompt formatting module, where it’s structured into a coherent input or “prompt” for the underlying foundation model (FM). This prompt defines the task at hand and contextualizes the data, ensuring the model has enough information to generate an accurate action. The agent then calls the foundational model for Task A. Foundational models, like large action models (LAM), process the prompt and generate a response based on the specific task.

The output from FM is then parsed by a response parser and prompt formatter. This parser interprets the model’s response, restructuring or reformatting it if needed to serve as input for follow-up steps. For complex operations requiring multiple steps, the agent may perform additional FM calls. This modular approach allows the agent to handle multi-step workflows and coordinate sequential steps before interacting with the environment.

An environment driver then takes the parsed responses from the above step and translates them into specific actions within the environment. This could mean executing commands, triggering alerts, or updating information within a system. This driver is usually customized to ensure compatibility with the environment, enabling seamless interaction and action.

Throughout these stages, the AI agent can gather feedback from the environment and adjust its approach. For example, if it receives unexpected outcomes, it can reformat its prompts or adjust its interpretation for future tasks. This adaptability makes AI agents more than static models; they are systems capable of evolving based on their interactions.

Types of AI Agents

AI Agents can be categorized across multiple dimensions, including their autonomy level, interaction style, and the functions or tasks they are designed to perform. From a business perspective, AI agents can also be categorized by scope and specialization.?

AI Agents by Scope?

• Horizontal AI Agents: These are general-purpose agents capable of handling a broad set of tasks across various industries or domains. Horizontal agents are often used in scenarios where versatility and adaptability are essential, such as in enterprise workflows.??
• Vertical AI Agents: Vertical agents are specialized for specific industries or functions where deep expertise and specialization are needed, such as banking, healthcare, legal, or manufacturing.

AI Agents by Autonomy

• Chatbots and Virtual Assistants (Low Autonomy): While chatbots and virtual assistants use natural language processing (NLP) to handle user queries and simple tasks, they typically follow pre-programmed scripts or decision trees. Their ability to act autonomously is limited to predefined scenarios, and they often require human supervision or fallback options for complex or unexpected inquiries. They are more reactive than proactive and can require frequent human guidance or updates.
• Automation Agents (Moderate Autonomy): Automation agents like robotic process automation (RPA) bots can automate repetitive tasks with minimal human intervention. These tasks are typically rule-based and require little human decision-making. While they excel at performing predefined tasks autonomously, they generally do not adapt to new tasks without reprogramming or manual configuration. Their autonomy comes from executing workflows and automating processes, but they still require human input to define the process and monitor performance.
• Decision-Making Agents (High Autonomy): Decision-making agents are the most autonomous because they analyze large sets of data and make decisions based on that analysis. They can often work independently in dynamic environments, adjusting their actions based on new information without direct human intervention. These agents can evaluate multiple variables, forecast outcomes, and optimize strategies, which makes them highly proactive in driving business decisions. The more advanced decision-making agents, especially those using AI and machine learning, can learn and improve their decision-making over time.

To select the right AI agent for your business, consider the tasks you need it to perform daily—whether simple automation, data-driven decision-making, or specialized industry applications—and choose an agent that aligns with your goals, required autonomy, and operational needs.?

AI Agents Drive Tangible ROI

At Orby, we believe time is humanity’s most precious resource. To give employees back critical time in their day through intelligent automation is a value prop previous technology has failed to delive. By automating routine tasks, AI agents free up human employees to focus on high-value work that requires creativity and critical thinking. This shift not only increases productivity but also enhances job satisfaction by eliminating monotonous activities.??

AI agents are capable of learning from every piece of data they process. Over time, this continuous learning leads to improved performance and the ability to offer deeper insights. They can uncover patterns, predict trends, and provide analytics that inform strategic decisions.

Moreover, the digital footprints left by AI agents during their operations generate valuable data that is not typically available from human activity alone. This new type of data can be analyzed to further refine processes and enhance decision-making.

AI agents offer transformative potential across virtually all sectors by enhancing efficiency, reducing costs, and enabling new capabilities. They can process vast amounts of information quickly and adapt to new inputs, making them valuable assets across a wide range of industries. At Orby, we’ve seen customers from across industries benefit from AI agents in finance automation, insurance and claims processing, HR, sales, and more.?

Conclusion

AI agents represent a significant leap forward in automation and intelligent systems. They offer businesses the opportunity to enhance efficiency, scalability, and customer satisfaction in ways previously unattainable. As industries continue to evolve, organizations that embrace AI agents will be better positioned to lead in innovation and competitiveness.

At Orby, we’re energized by the incredible impact our customers have achieved through their investments in AI Agents and smarter automation. And it’s only the beginning.?

This article was written by WILL (Dongxu) LU , CTO & co-Founder of Orby AI

Will is a visionary technologist and serial entrepreneur with deep expertise in AI and data platform engineering. As Co-Founder and CTO of Orby AI, he leads the company’s efforts in developing advanced agentic automation solutions that transform enterprise workflows. Prior to founding Orby, Will was a data platform leader at Google Cloud AI, where he co-founded and drove innovation for critical products like Contact Center AI, Doc AI, and the Enterprise Knowledge Graph. His work has consistently focused on leveraging AI to deliver meaningful impact and automation across industries. His extensive experience in creating impactful AI solutions underpins Orby’s vision of a world where people have more time for what matters.