How AI Agents Work

Understanding how an AI Agent works internally is the first step toward building one. At its core, an agent follows a simple loop: it observes, thinks, acts, and then observes again — repeating this cycle until the task is complete.

This process is called the Agent Loop, and it is the heartbeat of every AI Agent.

The Agent Loop Explained

Here is the step-by-step breakdown of how an AI Agent processes a task:

Step 1 — Receive Input (The Goal)

The agent receives a task or question from the user. This input is also called the prompt or goal.

Example: The user says — "Find the top 3 Python courses available online and list their prices."

Step 2 — Understand the Task (Reasoning)

The agent's brain (the LLM) reads the input and tries to understand:

• What is being asked?
• What steps are needed to complete this?
• What tools are available to help?

Agent's internal reasoning: "I need to search the web for Python courses, find their prices, and then present the top 3 results to the user."

Step 3 — Choose an Action (Tool Selection)

Based on its reasoning, the agent selects the most appropriate tool or action to use next.

Example: The agent decides to use the web search tool and searches for — "best Python courses online with price"

Step 4 — Execute the Action (Act)

The agent runs the chosen tool and gets back a result (also called an observation).

Example: The web search returns a list of courses from Udemy, Coursera, and YouTube with their details.

Step 5 — Observe the Result

The agent reads the output of the action and decides:

• Is the task complete?
• Is more information needed?
• Should another tool be used?

Step 6 — Repeat or Respond

If more work is needed, the agent goes back to Step 2 and reasons again. If the task is complete, the agent composes a final response and delivers it to the user.

Final response: "Here are the top 3 Python courses online: 1. Python Bootcamp on Udemy – ₹499, 2. Python for Everybody on Coursera – Free (Certificate paid), 3. CS50P by Harvard – Free"

Visual Representation of the Agent Loop

User Input (Goal)
       |
       v
  [ LLM Thinks ]
  "What should I do?"
       |
       v
  [ Choose Tool / Action ]
  "I'll use the web search tool"
       |
       v
  [ Execute Action ]
  "Searching: best Python courses..."
       |
       v
  [ Observe Result ]
  "Got search results with 10 courses"
       |
       v
  [ Is Task Complete? ]
  YES → Give Final Answer to User
  NO  → Go back to [ LLM Thinks ]

What Happens Inside the LLM?

The LLM (Large Language Model) is the thinking part of the agent. When the agent needs to reason, it sends a message to the LLM that looks something like this:

System: You are a helpful assistant with access to web search.
User: Find the top 3 Python courses with prices.
Available Tools: web_search(query)

What should you do next?

The LLM responds with something like:

I will use the web_search tool to find Python courses.
Action: web_search("top Python courses online with price 2024")

The agent framework reads this response, calls the actual web search function, and feeds the results back to the LLM for the next round of reasoning.

The Observe → Think → Act Cycle

This cycle is the foundation of all AI Agents, no matter how simple or complex. Even the most advanced agents follow this same pattern:

How the Agent Knows When to Stop

One important question is — how does an agent know when it has finished? The agent stops when:

• The LLM generates a final answer instead of choosing another tool
• A set maximum number of steps is reached (to avoid infinite loops)
• An error or condition tells it to stop

In code, this is usually managed by a loop that runs until the agent decides to output a final answer or hits the step limit.

A Simple Code Illustration

Here is a very simplified look at what the agent loop looks like in code (Python pseudocode):

# Define the goal
goal = "Find the weather in Delhi today"

# Start the loop
while True:
    # Step 1: Ask the LLM what to do next
    response = llm.think(goal, memory, available_tools)

    # Step 2: Did it decide to use a tool?
    if response.has_tool_call:
        tool_name = response.tool_name
        tool_input = response.tool_input

        # Step 3: Run the tool
        observation = run_tool(tool_name, tool_input)

        # Step 4: Add result to memory
        memory.add(observation)

    else:
        # No more tools needed — final answer ready
        print("Agent Response:", response.final_answer)
        break

This simple loop is the engine behind every AI Agent. The specific tools, the memory system, and the LLM used may vary — but the loop structure stays the same.

Role of Context / Memory in the Loop

As the agent goes through each loop iteration, it builds up a context — a running record of everything that has happened so far. This context includes:

• The original user goal
• Every tool called so far
• Every result received
• Any intermediate reasoning steps

This context is passed to the LLM each time it thinks, so the agent never "forgets" what it has already done during the current task.

Summary

An AI Agent works by continuously looping through three stages: Observe → Think → Act. It receives a goal, uses an LLM to reason about what to do, calls tools to take actions, observes the results, and repeats until the task is complete. This loop is simple in concept but incredibly powerful in practice — it is what allows agents to tackle complex, multi-step tasks autonomously.