What is an AI Agent?

What Is an AI Agent?

An AI agent is a software system that uses an AI model to pursue a goal by deciding which actions to take, using available tools, observing their results, and continuing until it can complete the task or needs help.

Agent vs Ordinary LLM Response

A language model can generate a useful answer from the prompt and context it receives. An agent adds a control loop around the model so the system can choose actions and react to their results.

The model is still important, but the system around the model is what gives an agent its ability to act.

The Core Components of an Agent

Most practical agents can be understood as a combination of several components. The exact implementation varies, but the responsibilities are similar.

The Agent Loop

The most useful mental model is a loop. The agent does not necessarily know the complete sequence of actions before it starts.

What Makes an Agent “Agentic”?

Not every application that calls an LLM is an agent. The important question is whether the system has meaningful control over the next action.

A system can also combine both: deterministic business rules can control high-risk steps while an agent handles flexible tasks.

Tools Give Agents Real Capabilities

Without tools, an agent is largely limited to information already available in its prompt or model. Tools let it interact with external systems.

A Simple Agent Mental Model in Python

The following example does not call a real LLM. It shows the control structure an agent application typically needs.

goal = "Find the current order status"

state = {"goal": goal, "result": None}

def choose_action(state):
    # A real agent would use an LLM or policy here.
    return "get_order_status"

def get_order_status():
    return "Order #1042 is shipped."

action = choose_action(state)

if action == "get_order_status":
    state["result"] = get_order_status()

print(state["result"])

The important part is the separation between decision and tool execution. In production, the tool layer should validate inputs and permissions before performing an action.

Example: Customer Support Agent

Imagine a support agent that receives: “My package has not arrived. Can you check it?” The agent may need several steps.

Example: Research Agent

A research agent may receive a goal such as “Compare three cloud providers for a small startup.” It could search approved sources, collect evidence, compare criteria, and produce a report.

This is different from asking a model to “write a comparison” from memory because the agent can perform external work before generating the final answer.

Agent vs Chatbot

A chatbot can contain tools and still behave agentically. The distinction is not the user interface; it is the system's ability to select and execute actions.

Agent vs RAG

RAG and agents solve different problems, but they can work together.

Agent Memory and State

Agents often need state so they can use information from earlier steps. “Memory” can mean several different things.

Good agent design stores only what is useful and permitted. More memory is not automatically better.

Planning Does Not Always Mean a Long Plan

An agent may decide one action at a time, create a short plan, or use a structured workflow. The right strategy depends on task complexity.

Agent Safety: Tools Need Boundaries

The most important production concern is that an agent can trigger actions. Tool access should therefore be narrower than the agent's general language ability.

When Should You Use an Agent?

Agent Cost and Latency

Agentic systems can perform multiple model calls and tool calls. That flexibility can increase cost and response time.

A Minimal Agent Loop in Python

Here is a simplified loop that shows the essential architecture without tying the lesson to a particular agent framework.

def agent_loop(goal, model, tools, max_steps=5):
    state = {"goal": goal, "history": []}

    for _ in range(max_steps):
        decision = model(state, tools)

        if decision["type"] == "final":
            return decision["answer"]

        if decision["type"] == "tool":
            name = decision["name"]
            arguments = decision["arguments"]

            result = tools[name](arguments)
            state["history"].append({
                "tool": name,
                "result": result,
            })

    return "The agent stopped before completing the task."

Production code should validate tool names and arguments, enforce permissions, handle failures, limit loops, and avoid exposing secrets to the model.

Common Agent Mistakes

How to Evaluate an Agent

Evaluate both the final outcome and the path used to reach it.

Keep representative tasks as an evaluation set and compare agent versions using the same scenarios.

Mini Project: Design a Simple AI Agent

Design an agent for a support application that can answer account questions and check order status.

Challenge: Draw the agent loop and list three situations where the agent should stop instead of trying another tool.

Quick Quiz

30-Second Recap

Key Takeaways

Good agent engineering is about giving the model useful choices inside safe boundaries. The next lessons will break down those choices, starting with how agents differ from ordinary chatbots and how tools are defined.