How AI Agents Actually Work: The Loop Behind Tools, Memory and Planning

Most people meet a language model as a chat box. You type a question, it writes a reply, and then it waits. That is a useful product, but it is not an agent.

An agent starts from a goal, not a single prompt-and-response. It looks at the current state, reasons about what would move the work forward, chooses an action, carries that action out through a tool, and reads what came back. Then it does the same thing again. The loop is the whole idea. The model is only the reasoning engine inside it. Tools, memory, planning, and a stop condition are what turn one chatbot reply into something that can finish a task.

If you want a longer walkthrough of this architecture, start with the beginner's guide on How Do AI Agents Work. This article stays with the loop itself.

A chatbot answers. An agent keeps going.

The practical difference is action. Ask a chatbot what flights exist and it can describe options. Give an agent a goal such as "find a flight under a budget and add it to my calendar," and it can search, compare, and — if you have given it the tools and the approval rules — complete the booking and create the event.

The model in both cases may be the same. What changes is the control flow. A chat product stops after text. An agent is allowed to call tools, observe the result, and continue without a new human message at every step.

That autonomy is also why agents fail differently. A wrong chatbot answer wastes time. A wrong agent action can send an email, write a file, or spend money. So a serious loop always has a boundary: a maximum number of steps, a clear definition of "done," and a point where certain actions wait for a person.

The loop: observe, reason, act, check

At a high level, the cycle looks like this:

1. Perceive the current state — the goal, the latest tool result, and any memory that still matters.
2. Reason about that state — what is true now, and what is still missing.
3. Plan the next step — not the entire future, just the next useful action.
4. Execute through a tool — search, read a file, run code, query an API, or send a message.
5. Check the result — did this move the work closer to the goal, or does the plan need to change?

Then repeat.

Here is the same idea as simplified pseudocode. It is a teaching sketch, not a framework:

goal = "Solve one useful task" state = initial_state(goal) for step in range(max_steps): action = agent.plan(goal, state) if action.needs_approval: await human.approve(action) result = tools.execute(action) state.observe(result) if state.goal_met or state.cannot_continue: break 

Three details in that sketch matter more than the syntax.

First, max_steps is not optional. Without a cap, a confused agent can keep calling tools forever.

Second, needs_approval is how you keep high-stakes actions on a leash. Reading a file may be fine. Publishing a post, booking a ticket, or deleting data usually should not be automatic.

Third, state.observe(result) is the difference between a script and an agent. A script assumes the last step worked. An agent treats the result as new information. If a search returns nothing, the next plan should change, not blindly proceed.

Five parts that make the loop possible

The same guide breaks an agent into five components: perception, reasoning, planning, tools, and memory. That split is useful because it stops the word "agent" from meaning "a smarter chatbot."

Perception is how the system sees. Agents receive structured inputs: your instruction, API responses, file contents, page text, database rows, and sometimes sensor data. This layer normalizes those inputs so the model can reason about them. If you ask it to analyze a spreadsheet, something has to read the file, notice the columns, and turn that into context. Without that step, the model is guessing.

Reasoning is the model itself, almost always a large language model. In a chatbot, the model usually makes one inference pass and returns text. In an agent, it runs again and again, deciding which action to take, which tool to call, and whether the last result helped. Common patterns include writing out a chain of thought before acting, and the ReAct style of alternating between thinking and doing. The important point is not the brand of the model. It is that the model is choosing the next move, not only writing a paragraph.

Planning is how a vague goal becomes concrete steps. "Analyze this CSV and email a report" is not one tool call. It is closer to: load the file, check the data, compute a summary, write a narrative, and send the message. Good planning is also willing to change. If the file is messy, cleaning it becomes a new first step. If a search returns no results, the agent may widen the query instead of inventing an answer.

Tools are the hands. Without them, the system can only talk. With them, it can browse, run code, read and write files, call APIs, query databases, or send mail. The model proposes a tool and arguments; the tool runs; the result comes back into the loop.

Memory is what remains after a step, so the next decision is not made in a vacuum.

Memory keeps the loop coherent

It helps to distinguish two kinds of memory. People often add a vector database and expect the agent to suddenly "learn." That usually disappoints.

Short-term memory is the working context of the current run: the goal, the messages, the recent tool results, and the current plan. It usually lives in the model's context window. When the task ends, it disappears.

Long-term memory is what should survive into a later run: a preference, a fact, a failed approach, or a useful procedure. Write down what will actually help later. Retrieve it before the next plan, not after the model has already decided. Keep memories scoped to a project so one task's notes do not leak into another.

A memory system that dumps raw logs forever will eventually get in the way. Periodic compression into a smaller set of durable facts is often more important than adding another retrieval service.

A script follows a path. An agent revises one.

A workflow or an RPA bot follows a predetermined path. If this field exists, fill it. If a column is missing, the bot breaks or silently fails. That rigidity is also a kind of reliability: the same input should produce the same action.

An agent uses the model to choose the next action from the latest observation. If a column is missing, it can notice, skip it, or ask for clarification. That flexibility is the point, and also the risk. The system is less predictable. You get usefulness on messy tasks, but you need guardrails: limited tools, approval for writes, and a way to tell whether the run actually worked.

The same contrast applies to chatbots. A chatbot can explain a process. An agent can attempt the process. For a side-by-side of that architectural difference, see AI Agent vs Chatbot.

Start with one bounded task

The temptation is to begin with a general assistant that can do anything. The more reliable starting point is one useful task, a small tool set, a step limit, and a human approval gate on anything irreversible.

If the path never changes, a workflow may be enough. If you only need an explanation, a chatbot is enough. An agent is worth the extra complexity when the work has to observe results and change course.

The loop itself is not mysterious: observe, reason, plan, act, check, repeat. The hard parts are the boundaries around it — which tools are allowed, what must be approved, how memory is kept small, and when the run should stop.