How AI Agents Work Under the Hood: A Clear Guide

AI agents are everywhere right now. They book meetings, write code, browse the web, and answer customer questions. From the outside, they look like magic. But they are not magic. They are systems, built from specific parts that work together in a specific order.

So what is an AI agent, exactly? At its core, it is a program that takes in information, decides what to do with it, and then does something. Perceive, plan, act. That is the loop. Understanding how AI agents work under the hood is the difference between using AI as a black box and actually building something that performs.

We are going to walk through each core component, keep the jargon low, and give you a clear mental model you can actually use.

The Brains of the Operation: LLMs and Their Role

Most modern AI agents use a large language model as their core reasoning engine. Think of the LLM as the part of the agent that thinks.

What is an LLM, in plain terms? It is a model trained on an enormous amount of text. It learned patterns, facts, relationships, and language from that data. Now it can read a prompt and generate a response that fits the context.

That sounds simple. But the power is in what that capability enables.

When you give an LLM a goal, it can reason through the steps needed to reach it. It can interpret ambiguous instructions. It can generate a plan. It can even evaluate whether a plan is working.

We like to think of the LLM as the agent's internal consultant. When the agent hits a problem, it asks the LLM: "What should I do here?" The LLM gives a response. The agent acts on it.

But here is the thing. An LLM on its own is not an agent. It is a very smart text predictor sitting in a box. It cannot browse the web. It cannot send an email. It cannot check your database. It just generates text.

To become an agent, the LLM needs structure around it. It needs inputs, tools, memory, and a loop that keeps it moving toward a goal. The LLM is central, yes. But it is one piece of a larger system.

Perception and Observation: How Agents See the World

Before an agent can do anything useful, it needs information. That is where perception comes in.

Perception is the process of receiving data from the environment and converting it into something the agent can work with. The "environment" could be a website, a database, a user's message, a file, an API response, or a live data stream.

Here are some real examples of how agents perceive:

• Reading a web page: The agent calls a browser tool, gets back the HTML, and the LLM extracts what is relevant.
• Querying a database: The agent runs a SQL query and receives structured rows of data.
• Processing user input: A person types a message. The agent reads it as a string and passes it to the LLM with context.
• Analyzing an image: A multimodal agent can take a screenshot or photo and interpret its contents.

One thing we see people overlook is the difference between structured and unstructured data. A database row is structured. A block of scraped web text is not. Agents often need to do a conversion step, turning messy raw input into clean, usable context before the LLM can reason about it.

Agents do not perceive directly. They use tools and functions to retrieve information. A "search" tool, a "read file" tool, a "get calendar events" tool. Each one is a defined function the agent can call.

An agent is only as good as the information it can access. If the perception layer is weak or incomplete, everything downstream suffers. We spend a lot of time on this layer when we build agents for clients.

Planning and Decision-Making: The Agent's Internal Monologue

Once the agent has information, it needs to figure out what to do with it. This is the planning phase, and it is where things get interesting.

It starts with the system prompt. This is a set of instructions given to the LLM before any user input arrives. It tells the agent who it is, what it can do, how it should behave, and what tools it has access to. Think of it as the agent's job description.

From there, the agent enters a reasoning loop. One popular framework for this is called ReAct, which stands for Reason and Act. The loop looks like this:

1. Observe: What information do I have right now?
2. Think: What does this mean? What do I need to do next?
3. Act: Which tool should I call? What input should I give it?
4. Observe again: What did the tool return? Did it work? See also: product description AI workflow setup.

This loop repeats until the agent reaches its goal or hits a stopping condition.

Let's walk through a simple example. A user asks: "What is the weather in London right now?"

• The agent observes the question.
• It thinks: I need current weather data. I have a weather API tool.
• It acts: It calls the weather tool with the input "London".
• It observes the result and generates a response.

Memory also plays a role here. If the agent remembers that this user always asks about London, it can skip a clarification step next time. Past context shapes future decisions.

This planning phase is where intelligent behavior actually happens. It is not just command execution. The agent is genuinely working through a problem, step by step.

Action and Execution: Making Things Happen

Perceiving and planning mean nothing if the agent cannot act. This is where the rubber meets the road.

Actions are the things an agent does in its environment. These are executed through tools, which are pre-defined functions the agent can call. The LLM decides which tool to use and what inputs to pass it. The tool runs. The result comes back.

Here are some common actions agents take:

• Sending emails or Slack messages
• Writing and running code
• Updating a CRM or database record
• Browsing the web and extracting data
• Generating a document or report
• Calling a third-party API
• Triggering another workflow or agent

Tool orchestration is the skill of choosing the right tool for the right job at the right time. A well-built agent does not just have tools available. It knows when to use them, in what order, and how to handle it when one fails.

After each action, the agent goes back to the perception phase. It checks the result. Did the email send? Did the API return what was expected? If something went wrong, it adjusts its plan and tries again.

This feedback loop is what separates a real agent from a simple script. A script runs in a straight line. An agent adapts. See also: GrowthSpike.

The true power here is in combining strong reasoning with a broad set of tools. The LLM provides the judgment. The tools provide the reach. Together, they can handle complex, multi-step tasks that no single function could manage on its own.

Memory and Learning: Getting Smarter Over Time

A basic AI agent is stateless. Every conversation starts from scratch. That works for simple tasks, but it falls apart fast when you need continuity.

Advanced agents have memory, and there are two types that matter.

Short-term memory is the context window. This is everything the agent can "see" in a single session: the conversation so far, the tools it has called, the results it got back. It is temporary. When the session ends, it is gone.

Short-term memory is what allows an agent to hold a multi-turn conversation without losing track of what was said three messages ago. It is also what lets the agent reference an earlier result when making a new decision.

Long-term memory is different. This is persistent storage, often a vector database or a knowledge base. The agent can write information here and retrieve it later, across sessions.

This is where things like user preferences, past decisions, learned facts, and historical context live. An agent with long-term memory can remember that a client prefers weekly reports on Fridays, or that a specific API tends to fail on weekends.

The mechanism that connects long-term memory to the LLM is called retrieval-augmented generation, or RAG. When the agent needs to reason about something, it searches its memory for relevant information, pulls it into the context window, and gives the LLM that extra context to work with.

This is not the same as retraining the model. The LLM itself does not change. But its knowledge base grows, and its behavior becomes more accurate and personalized over time.

Memory is what turns a one-off task executor into a system that actually improves the more you use it. Without it, you are starting from zero every time. See also: how AI agents.

Building Smarter Systems: The Future of AI Agents

Let's bring it all together.

An AI agent is not one thing. It is a system made of interconnected parts:

• LLM: The reasoning engine that thinks through problems and generates plans.
• Perception: The layer that gathers information from the environment using tools.
• Planning: The loop where the agent decides what to do next, step by step.
• Action: The execution of tasks through tools that interact with the real world.
• Memory: The short-term and long-term storage that gives the agent continuity and context.

None of these parts work well in isolation. The magic, if you want to call it that, comes from how they connect.

For businesses and developers, understanding these mechanics matters. When you know what each layer does, you can diagnose why an agent is underperforming. Is the perception layer missing a key data source? Is the planning loop getting stuck? Are tools failing silently? Is memory not being written or retrieved correctly?

You can also make smarter decisions about what to build. Not every problem needs a full agent. Sometimes a simple tool call is enough. But when you need something that can handle ambiguity, adapt to new information, and run multi-step workflows without hand-holding, an agent with all five components is the right call.

As LLMs get better, as tools become more reliable, and as memory systems scale, the gap between what agents can do and what humans do manually will keep shrinking.

If you are thinking about building AI agents for your business, start by mapping out these five layers for your specific use case. What information does the agent need to perceive? What decisions does it need to make? What actions does it need to take? What does it need to remember?

Answer those questions, and you have the foundation of something that actually works. We are here to help you build the rest.