Beyond Chatbots: The Architecture of Modern AI Agents

From Answering Questions to Achieving Goals

For the past few years, our interaction with Large Language Models (LLMs) has been primarily conversational. We ask a question, it provides an answer. But a far more powerful paradigm is emerging: AI Agents.

Unlike a passive chatbot, an AI agent is an autonomous system that can reason, create plans, and use tools to achieve a specific goal. It's the difference between asking for a weather forecast and saying, "Book me a flight to Hawaii for next week, find a hotel within my budget, and add it to my calendar."

To understand how this is possible, we need to look beyond the LLM itself and into the architecture that gives it agency.

1. The Core Agentic Loop: Observe, Think, Act

The "brain" of an agent is its core execution loop. While there are many variations, most are based on the ReAct (Reason + Act) framework. Instead of just generating a final answer, the LLM is prompted to think step-by-step and decide on its next action.

The loop looks like this:

1. Observe: The agent is given an initial goal and observes its current state (e.g., "I need to book a flight," "I have no flight information yet").
2. Think: The LLM reasons about the next step. It outputs its thought process and decides on an action. For example: "My goal is to book a flight. I don't have flight prices. I should use the search_flights tool."
3. Act: The agent's runtime executes the action decided by the LLM (e.g., it calls the search_flights API with the right parameters).
4. Observe (Again): The result of the action (the flight data or an error) is fed back into the loop as a new observation.

This cycle repeats until the agent determines that the original goal has been accomplished.

// A simplified pseudo-code representation of an agentic loop
async function runAgent(goal: string) {
  let observation = 'Initial state is empty.';
  let history = [];
 
  while (!isGoalComplete(history, goal)) {
    // Think: The LLM generates a thought process and an action
    const { thought, action } = await llm.generatePlan(goal, history, observation);
    history.push(`Thought: ${thought}`);
 
    // Act: The runtime executes the action
    const result = await executeTool(action.toolName, action.toolInput);
    observation = `Action '${action.toolName}' returned: ${result}`;
    history.push(`Observation: ${observation}`);
  }
 
  return 'Goal accomplished.';
}

2. Tool Use: Giving the Agent "Hands"

An LLM is just a text generator; it can't browse the web, run code, or access a database. Tools are what give an agent the ability to interact with the outside world.

A tool is simply a function that the agent can decide to call. Each tool is given a name and a description, which the LLM uses to understand what it does.

• Web Search: To get up-to-date information.
• API Calls: To interact with services like Google Calendar, Stripe, or a company's internal database.
• Code Execution: To run Python scripts for data analysis or file manipulation.
• Database Queries: To retrieve structured information.

When the LLM decides to use a tool, it generates a specific output, often in JSON format, that the agent's runtime can parse and execute. The output of the tool is then converted back into text and fed into the next step of the agent's loop.

3. Memory: Learning and Remembering

To perform complex, multi-step tasks, an agent needs memory. Without it, every turn of the loop would be independent of the last. Memory in AI agents typically comes in two forms:

Short-Term Memory

This is the "working memory" of the agent. It's the history of the current task, including all the previous thoughts, actions, and observations. This history is included in the prompt to the LLM on each step, providing the context it needs to make its next decision.

Long-Term Memory

For an agent to learn across multiple tasks or conversations, it needs a long-term memory. This is most commonly implemented using a vector database.

The process works like this:

1. Store: Important pieces of information (like the results of a successful task or a key piece of user feedback) are converted into numerical representations called embeddings. These embeddings are stored in a vector database.
2. Retrieve: At the start of a new task, the agent's goal is also converted into an embedding. It then queries the vector database to find the most similar (i.e., most relevant) memories from its past.
3. Augment: These retrieved memories are added to the agent's short-term memory, giving it relevant context from past experiences to inform its current plan.

The Future is Agentic

Frameworks like LangGraph and CrewAI are making it easier than ever to build these sophisticated systems, even orchestrating multiple agents that collaborate to solve problems. We are rapidly moving from a world of simple, stateless AI to one where autonomous, stateful agents can act as true digital colleagues, capable of executing complex workflows on our behalf.