What Are AI Agents?

What Are AI Agents?

An AI agent is a software system that perceives its environment, reasons about a goal, and takes actions to achieve that goal — often without requiring a human to approve each individual step. The word "agent" comes from the Latin agere (to act), and that captures the essence: these systems don't just respond to queries, they act in the world.

The Perception-Reasoning-Action Loop

At the heart of every agent is a loop:

1. Perceive — gather information from the environment (user input, tool results, database records, web pages)
2. Reason — use an LLM to decide what to do next
3. Act — execute a tool call, write a file, send an API request
4. Observe — receive the result of the action
5. Repeat — feed the observation back into step 2

This loop continues until the agent reaches its goal or hits a stopping condition (max iterations, user interrupt, explicit finish action).

# Simplified agent loop pseudocode
def run_agent(goal: str, tools: list[Tool], max_steps: int = 10) -> str:
    messages = [{"role": "user", "content": goal}]
    
    for step in range(max_steps):
        # Reason: ask the LLM what to do
        response = llm.chat(messages, tools=tools)
        
        if response.finish_reason == "stop":
            # Agent produced a final answer
            return response.content
        
        if response.finish_reason == "tool_calls":
            # Act: execute each requested tool
            for tool_call in response.tool_calls:
                result = execute_tool(tool_call)
                # Observe: add result to message history
                messages.append({"role": "tool", "content": result})
    
    return "Max iterations reached without a final answer."

What Makes Something an Agent (vs. a Chatbot)?

A standard chatbot takes one input and produces one output — it's stateless and reactive. An agent is different in three key ways:

The Environment

An agent's environment is everything it can perceive and change. This might include:

• APIs: weather services, databases, email, calendar
• File systems: reading and writing documents, code, data
• Web browsers: searching, scraping, form submission
• Other agents: sub-agents it can delegate tasks to
• Code interpreters: running Python/JavaScript to perform calculations

Why Agents Now?

Two advances made modern AI agents practical:

1. LLMs can follow complex instructions — models like GPT-4, Claude 3, and Gemini understand nuanced tool schemas and can reason across many steps without losing track of the goal.
2. Function calling APIs — providers now offer structured APIs where the model returns a typed function call object instead of free text, making tool invocation reliable enough for production systems.

The combination of powerful reasoning and reliable tool interfaces is what transformed AI from a question-answering system into an autonomous problem-solver.