What Is an AI Agent? Architecture of Planner, Tools, Memory and the Control Loop

"AI agent" is used for everything from a chatbot with a search button to software that completes multi-hour tasks on its own. This guide gives a precise working definition, shows the parts every agent shares, and helps you decide whether you need one at all.

A working definition

An agent is a program in which a language model repeatedly decides what to do next, takes an action through a tool, observes the result and continues until it decides the task is finished. The defining feature is that the model controls the flow. In a conventional program, the developer writes the sequence of steps. In an agent, the model chooses them at run time.

That separates three common designs:

Workflows are predictable and cheaper. Agents are flexible but harder to test and secure. Many successful products are workflows with a model inside, not true agents.

The five components

1. The model. The reasoning engine. It reads the task and the history so far and outputs either a final answer or a request to call a tool.

2. Tools. Functions the agent may call: search a knowledge base, query a database, run code, send a message, call an API. Each tool has a name, a description and a typed input schema. The description matters, because it is the only instruction the model has about when to use the tool.

3. The control loop. Ordinary code that runs the cycle:

let messages = [{ role: 'user', content: task }];
for (let step = 0; step < MAX_STEPS; step++) {
  const reply = await model.generate({ messages, tools });
  if (reply.finalAnswer) return reply.finalAnswer;
  for (const call of reply.toolCalls) {
    const result = await runTool(call.name, call.arguments); // validate first
    messages.push({ role: 'tool', name: call.name, content: result });
  }
}
throw new Error('Step limit reached');

Notice the step limit. Without it, a confused agent can loop indefinitely and spend money.

4. Memory. Short-term memory is the running conversation, including tool results, held in the model's context window. Long-term memory is stored outside the model, often in a database or vector store, and retrieved when relevant. Memory is also an attack surface, because anything written into it can be read back later.

5. Guardrails. Code around the loop: input validation, permission checks, approval steps, budgets and logging. These are what make an agent safe to run, and none of them are the model's job.

The reasoning pattern

Most agents follow a variant of "reason, act, observe" (the ReAct pattern from the research literature): the model briefly reasons about what it knows, picks an action, receives an observation, and updates. Some agents first write a plan (a list of steps), then execute it, revising when something fails. Planning helps on long tasks; a simple loop is enough for short ones.

When an agent is the wrong choice

Use the simplest design that works. Prefer:

• A single call when the task is one transformation of text.
• A workflow when the steps are known and stable, even if each step uses a model.
• An agent only when the path genuinely depends on what is discovered along the way, such as debugging, research across unknown sources, or multi-system investigation.

Agents cost more (many model calls per task), are slower, and fail in more varied ways. If you can draw the flowchart in advance, draw it and code it.

Failure modes to expect

• Wrong tool or wrong arguments, often because a tool description is vague.
• Loops where the agent repeats the same failing action.
• Context overflow, where long tool outputs push out the original task.
• Confident but wrong conclusions, where the agent stops early and declares success.
• Prompt injection through anything it reads (see Defending LLM Applications Against Prompt Injection).

Design principles that help

1. Give each tool one clear job and a precise description with examples.
2. Return small, structured tool results rather than raw dumps.
3. Set limits on steps, time, tokens and spend.
4. Make irreversible actions require approval (human-in-the-loop design).
5. Test with realistic tasks and track success rates (evaluating agents).
6. Apply least privilege to every tool (secure agentic workflows).

Understanding that an agent is just a model in a loop with tools makes it easier to reason about both its capabilities and its risks: everything it can do is something you gave it access to.