AI agents are the talk of the technology world, and for good reason. These are the tools that promise to act on our behalf, autonomously handling tasks like booking complex travel, researching market trends, or even building software. They represent a significant leap forward, moving generative AI from a simple chatbot to an active participant in the digital world. But as with any fast-moving technology, a sense of mystery surrounds how these agents actually work. Their ability to plan, act, and complete multi-step goals can seem almost magical or hopelessly opaque. How do they really figure out what to do next? How do they remember what you want? And how do they do it all securely? To cut through the noise, we can look under the hood at the core components that make these systems tick. Drawing on insights from a recent article by Amazon's Marc Brooker on their AgentCore framework, we can demystify agentic AI by revealing four surprising truths about how they operate. They operate on a simple ‘think, act, observe’ loop. At the heart of what appears to be complex autonomous behavior is a surprisingly simple and powerful process. While other architectures exist, the core of most successful agentic systems today is built on a model called ReAct, which stands for Reasoning + Action. This isn't a form of conscious thought, but rather a straightforward, continuous loop. They operate on a simple ‘think, act, observe’ loop. At the heart of what appears to be complex autonomous behavior is a surprisingly simple and powerful process. While other architectures exist, the core of most successful agentic systems today is built on a model called ReAct, which stands for Reasoning + Action. This isn't a form of conscious thought, but rather a straightforward, continuous loop. The process works like this: Thought: The agent forms a simple plan, like "I'll use the map function to locate nearby restaurants." Action: It executes that plan by performing an action, such as calling a tool. Observation: It observes the result of that action, receiving data like, "There are two pizza places and one Indian restaurant within two blocks of the movie theater." This thought-action-observation sequence repeats, allowing the agent to continuously "ratchet toward accomplishment of the goal." Critically, this loop allows the agent to self-correct and handle unexpected outcomes; a fundamental requirement for autonomy. This iterative approach is what allows agents to tackle ambiguous goals that would stall traditional programs, moving them from mere calculators to genuine problem-solvers. They have both a short-term scratchpad and a long-term memory. For an agent to be useful, it needs memory. But it turns out agents rely on two very different kinds of memory for different purposes, solving a crucial problem of focus and efficiency. They have both a short-term scratchpad and a long-term memory. For an agent to be useful, it needs memory. But it turns out agents rely on two very different kinds of memory for different purposes, solving a crucial problem of focus and efficiency. Short-term memory acts as a temporary workspace or scratchpad for the current task. For example, if an agent asks a map tool for nearby restaurants and gets a list of two dozen, it would be a mistake to load all of that information into the AI model's active context (think of this as the model's immediate working memory or 'attention'). As the source text explains, doing so "could wreak havoc with next-word probabilities." Instead, the agent stores the full list in its short-term memory, acting as a buffer to protect the LLM's finite attention. It then pulls just one or two relevant options into its focus at a time, ensuring it can work efficiently without getting overwhelmed. Long-term memory is what allows an agent to remember your preferences across different sessions. If you told a booking agent last week what kind of food you like, you shouldn't have to repeat yourself this week. What’s surprising, however, is that agents typically don't create these memories themselves. After a session is complete, the entire conversation is passed to a separate AI model whose job is to process the interaction, extract key preferences, and update the agent's long-term memory for future use. They can point and click on websites, just like you. Agents typically interact with the digital world through tools that have clearly defined APIs (Application Programming Interfaces), which act like a structured menu for software to talk to other software. But what happens when the information an agent needs is on a website with no public API? The answer is surprisingly low-tech: the agent can use it like a person would. They can point and click on websites, just like you. Agents typically interact with the digital world through tools that have clearly defined APIs (Application Programming Interfaces), which act like a structured menu for software to talk to other software. But what happens when the information an agent needs is on a website with no public API? The answer is surprisingly low-tech: the agent can use it like a person would. This capability, referred to as "computer use," allows an agent to interact with a standard website by pointing, clicking, and filling out forms. This is enabled by services like Amazon's Nova Act working in conjunction with tools like AgentCore’s secure Browser. This simple but powerful capability instantly expands an agent's toolkit from a limited set of pre-defined functions to nearly the entire internet. Computer use makes any website a potential tool for agents, opening up decades of content and valuable services that aren’t yet available directly through APIs. Each session runs in its own tiny, ultra-secure 'microVM'. Running thousands or millions of different AI agents for different users on shared servers presents a massive security and efficiency challenge. Historically, developers faced a difficult trade-off: use containerization, which was efficient but offered lower security, or use virtual machines (VMs), which were secure but came with a lot of computational overhead. Each session runs in its own tiny, ultra-secure 'microVM'. Running thousands or millions of different AI agents for different users on shared servers presents a massive security and efficiency challenge. Historically, developers faced a difficult trade-off: use containerization, which was efficient but offered lower security, or use virtual machines (VMs), which were secure but came with a lot of computational overhead. A technology called Firecracker elegantly solves this problem. It creates "microVMs" that offer the robust, hardware-level security of a traditional VM but with the lightweight efficiency of a container. Frameworks like AgentCore use this to enable a model called "session-based isolation." Every time you start a conversation with an agent, it is assigned its very own, completely isolated Firecracker microVM. This tiny, disposable micro-computer runs the agent's code for the duration of your session. The most important part? When your session ends, the microVM is completely destroyed, along with everything in it. This ensures that each user interaction is fully contained and secure, allowing agentic systems to run safely for millions of users at massive scale. From Magic to Mechanics AI agents are not magic. They are sophisticated systems, but they are built from a collection of understandable and practical components. It's not a single magical brain, but a clever factory line: a simple loop acts as the engine, specialized memory systems feed it the right information, versatile tools connect it to the world, and a secure, disposable workspace ensures it can operate safely at massive scale. By understanding these core mechanics, we can move past the hype and see agentic AI for what it is: a powerful new form of software. This leaves us with a compelling question for the future: Now that we understand the core mechanics, what truly complex problems will we be able to solve as each of these components becomes exponentially more powerful? Podcast: Apple: HERE Spotify: HERE

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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