Welcome back to The Agentic Shift. This series is my attempt to map the new territory of agentic AI as it unfolds—a shift as fundamental as the move from desktop to mobile. We’re on a journey to understand how AI is evolving from a passive tool that creates to an active partner that does. Together, we’ll dissect the anatomy of an agent, explore how it thinks and remembers, examine the tools it uses to act, and grapple with the challenges of guiding it safely.
In our first post, we introduced this new age of agents. Now, it’s time to get our hands dirty and look under the hood.
From Maps to Navigators
I love maps. I always have. As a kid, I’d spread them out on the floor, tracing roads with my finger, just to understand the shape of a place. I love the ritual of folding them just right. For years, I kept a stack of them in my car. I even had the incredible fortune to work on Google Maps for nearly a decade.
Given all that, you’d think my sense of direction would be impeccable. Well, it isn’t. I could get lost in a paper bag with one opening. For me, a map is a beautiful tool for understanding, but a terrible one for navigating. It gives you all the data, but you have to do the hard work of figuring out where you are, where you’re going, and what to do when you inevitably take a wrong turn.
A GPS navigator, on the other hand, is a different beast entirely. It’s an active partner. You give it a goal—”Get me to the airport”—and it takes on the cognitive load. It doesn’t use AI in the way we’re going to be talking about it in this series, but it has the key characteristics of an agentic system. It senses the current state of the world through traffic data. It thinks about the most efficient path. And it uses its tools to act, giving you turn-by-turn directions. If it senses a problem, it proactively finds another way.
That leap—from a static tool to an active, goal-oriented partner—is the very essence of the “agentic shift.” And just like a GPS, an AI agent is defined by its fundamental anatomy: how it perceives its world, how it thinks, and how it acts.
Defining the Agent: More Than a Smart Tool
Before we go any further, let’s address the elephant in the room. The term “AI agent” is, as technologist Simon Willison has noted, “infuriatingly vague.” Different people use it to mean different things. For some, it’s an “LLM autonomously using tools in a loop.” For others, it’s a system that can “plan an approach and then run tools… until a goal is achieved.”
For our purposes in this series, we’ll establish a simple, core principle: an agent isn’t just a model; it’s a system built around a model. It’s a complete entity with distinct parts that work together. To understand it, we need to look at its three anatomical pillars:
- Perception: The Senses
- Reasoning/Cognition: The Brain
- Action: The Hands
But why is this happening now? After all, we’ve had automation and bots for years. The difference lies in a powerful technological convergence. First, the “brain” got a massive upgrade; recent large models are capable of genuine reasoning and planning. Second, the digital world has become almost universally accessible via APIs, giving the agent’s “senses” and “hands” a world of information to perceive and a universe of tools to act upon. This combination is what makes the current moment so transformative.
The Anatomy, Piece by Piece
Let’s break down what each of these parts actually does.
Perception (The Senses)
First, how does an agent understand its environment? When we talk about an agent’s senses, we’re not talking about cameras or microphones. An AI agent’s environment is digital. Its perception comes from its ability to access information through APIs, data streams, and file systems. It might “see” the latest financial data by calling a stock market API, or “read” a user’s notes by accessing a local file. This is its window into the digital world.
Reasoning/Cognition (The Brain)
At the heart of every agent is its brain: a large model. This is the component that takes the information from its senses, considers the overall goal, and creates a plan. The model is the decision-maker. In Part 2 of this series, we’ll dive deep into how it thinks using different cognitive patterns, and in Part 3, we’ll explore the critical role of memory. For now, just know this is the part that makes the choices.
Action (The Hands)
An agent that can perceive and think is still just an observer. To be an agent, it must be able to do things. The agent’s “hands” are the tools it has been given. These tools are almost always APIs that allow it to perform actions: writing to a file, sending an email, searching the web, or running a piece of code. This is where the agent moves from thinking to acting. This creates a dynamic feedback loop: it acts, perceives the results of that action, and then reasons about what to do next. This cycle is the engine of an agent. This concept is so central that we’ll dedicate Part 4 entirely to the agent’s ‘toolkit’ and Part 5 to the art of writing the instructions that guide its actions.
The “Agentic” Spark: What Makes It Different?
These three parts—perception, reasoning, and action—are the building blocks. But what truly makes a system agentic are the emergent properties that come from combining them:
- Autonomy: It can operate without constant, step-by-step human intervention. This doesn’t make the human irrelevant; it changes the nature of our collaboration from micromanagement to high-level direction. It doesn’t need to be told how to do something, just what the goal is.
- Goal-Orientation: It’s driven by a high-level objective, not just a single command. The goal isn’t “search for flights”; it’s “plan my business trip to Singapore.”
- Proactivity: It can take initiative. Like the GPS that reroutes you around traffic, an agent can adapt its plan when it perceives changes in its environment.
This combination of autonomy and proactivity is incredibly powerful, but it also introduces new challenges we have to solve. In Part 6, we’ll discuss how to build in the necessary guardrails to ensure agents act safely and securely.
A Simple Agent in Action: The Weather Forecaster
Let’s tie this all together with a simple example. Imagine an agent whose goal is to answer the question: “Will I need an umbrella tomorrow?”
- Goal: The agent is given its objective.
- Perception: It uses its senses—a weather API—to get the forecast for your location.
- Reasoning: Its brain processes the data it perceived: “80% chance of precipitation.” It connects this data to the goal and concludes that rain is likely and an umbrella would be useful.
- Action: It uses its hands—a notification API—to send a message to your phone: “Looks like rain tomorrow, don’t forget your umbrella!”
- Loop: The action is complete. The agent now waits, ready to perceive new information or receive a new goal.
This simple loop is the foundation of every agent, from this basic forecaster to the most complex systems being built today.
Conclusion: The Foundation is Set
So, what is an AI agent? At its core, it’s a system with a reasoning brain (a large model) connected to a digital environment through a dynamic loop of perception and action.
Understanding this anatomy isn’t just an academic exercise. For anyone looking to build, manage, or work alongside these new systems, this is the essential first step. It gives us a shared language and a mental model for everything that follows.
Now that we’ve assembled the basic anatomy of an agent, the rest of this series will be about bringing it to life. In Part 2, we’ll explore the fascinating ways an agent thinks, and from there, we’ll cover everything from memory and tools to safety and even how multiple agents can collaborate to solve complex problems. The foundation is set, and the exciting part is just beginning.