What are AI agents?

Summary slides

The core loop every AI agent runs, regardless of its specific implementation

Every AI agent, whatever its specific domain or architecture, runs some version of the same underlying loop, observe the current state of the task, reason about what to do next given that state and the task’s goal, take an action, typically a tool call, and incorporate that action’s result back into the agent’s understanding before repeating the cycle. This loop continues until the agent determines the task is complete or some stopping condition is reached, and understanding this loop as the defining structure of an AI agent, rather than any particular tool or capability, is what distinguishes an agent from a simple single-turn model call.

This loop connects directly to the reasoning-and-acting pattern covered throughout this collection’s discussion of ReAct agents, where each cycle’s reasoning step is made explicit rather than left implicit, and understanding the general loop first makes the more specific implementation patterns this collection covers considerably easier to place, each is a particular way of structuring or refining this same underlying cycle.

What actually separates an AI agent from a chatbot or a single model call

A chatbot that responds to a single message with a single reply, however sophisticated that reply’s reasoning, isn’t an agent unless it can take multiple, sequential actions toward a goal and adjust its next step based on what a previous action revealed, connecting directly to the workflow distinction covered throughout this collection’s discussion of agents versus workflows. The defining feature isn’t the underlying model’s capability, the same model can power both a simple chatbot and a genuine agent, it’s whether the surrounding system is built to let that model make a sequence of its own decisions rather than produce one bounded response.

This distinction matters because “AI agent” gets applied loosely to systems that don’t actually exhibit this multi-step, self-directed behavior, and being precise about it helps set the right expectations, a genuine agent can handle a task whose exact steps weren’t known in advance, while a system that only responds to single prompts, no matter how well, cannot.

The components that make up a functioning AI agent

Beyond the underlying language model doing the reasoning, a functioning AI agent needs the tool use covered throughout this collection’s dedicated article, giving it a way to actually affect and observe the world beyond generating text, the memory covered throughout this collection’s broader discussion of that topic, letting it retain relevant information across the steps of a task rather than starting fresh each time, and the planning covered elsewhere in this collection, giving it a coherent structure for sequencing actions toward the task’s actual goal rather than reacting to each step in isolation.

None of these components alone constitutes an agent, connecting directly to the architecture discussion covered throughout this collection’s article on AI agent architecture, an agent is specifically the combination of all of them working together through the reasoning loop described above, and a system missing any one of these pieces, tool use without memory, memory without planning, tends to handle real, extended tasks considerably less reliably than one where all the pieces are present and well-integrated.

Why an agent’s tool set defines the actual boundaries of what it can do

An AI agent’s reasoning capability determines how well it decides what to do, but its available tools determine what it’s actually capable of doing at all, connecting directly to the curated tool selection covered throughout this collection’s discussion of agent tool use. An agent reasoning brilliantly about how to check a customer’s account status still can’t do it without a tool that actually queries that account data, the underlying model’s intelligence is necessary but never sufficient, the agent’s tool set is what turns that intelligence into genuine capability.

This is why designing an agent’s tool set deserves the same deliberate attention given to its underlying model choice, an agent with a narrow, well-matched set of tools for its actual task tends to perform more reliably than one given a sprawling, loosely relevant tool set, since tool selection quality itself degrades once an agent has too many options to reliably choose among.

Why AI agents need verification precisely because their actions have real consequences

Unlike a single model response that a person reviews before acting on it, an AI agent’s actions, sending a message, modifying a record, executing a transaction, can take effect directly, connecting to the verification discipline covered throughout this collection’s broader discussion of agent reliability and autonomous AI agents. This is why building a trustworthy AI agent means treating verification, checking a proposed action before it executes, reviewing outcomes after it completes, as a core design requirement rather than an optional addition layered on once the agent’s basic capability works.

The autonomy level covered throughout this collection’s dedicated discussion of that topic is the practical lever for calibrating this verification need, a task with low-stakes, reversible actions can reasonably grant an agent more independence, while a task with consequential, hard-to-reverse actions needs correspondingly more human oversight built into the agent’s actual operating loop, not bolted on afterward as an afterthought.

Common mistakes teams make around AI agents

1. Calling any system that uses a language model an “agent,” without distinguishing genuine multi-step, self-directed action from a single bounded response.

2. Building an agent’s reasoning capability well while neglecting its tool set, leaving it intelligent but genuinely unable to affect the task it’s meant to handle.

3. Treating memory, tool use, and planning as separable, optional add-ons rather than components that need to work together through one coherent reasoning loop.

4. Granting an agent broad autonomy without building the verification and oversight its actual actions’ consequences actually warrant.

5. Expanding an agent’s tool set indefinitely without recognizing the tool-selection degradation that results once its available options grow too broad.

What connects these mistakes is treating “AI agent” as a label to apply loosely rather than a specific architecture with real requirements, an agent is defined by its reasoning loop, its tools, its memory, and the verification wrapped around its actions, and building one well means attending deliberately to each of these components rather than assuming a capable underlying model is sufficient on its own.

The deeper point about AI agents is that what makes them valuable, the capacity to take a sequence of their own decisions and actions toward a goal, is exactly what makes them require more careful engineering than a single model call, tool access that actually matches the task, memory that persists what matters, and verification proportional to what’s actually at stake, and an agent built with this full picture in mind is what separates a system genuinely capable of handling real, extended work from one that merely demonstrates impressive reasoning in isolated examples.