What is an AI Agent?
AI agents are autonomous systems that perceive their environment, reason about it, make plans, and take actions to achieve goals. They represent the shift from AI as a tool you query to AI as a collaborator that acts.
From Chatbots to Autonomous Systems
A standard LLM is reactive: you send a message, it generates a response, and the interaction ends. It does not take actions in the world, does not remember what happened yesterday, and does not pursue goals over time. It is a brilliant conversation partner, but it cannot do anything beyond producing text.
An AI agent is fundamentally different. It is an AI system designed to operate autonomously, taking a sequence of actions to accomplish a goal. An agent can browse the web, write and execute code, query databases, send emails, manage files, and interact with APIs. It decides what to do next based on what it has observed and what it needs to achieve.
The key distinction: a chatbot answers questions. An agent completes tasks.
The Agent Loop: Observe, Think, Act, Learn
Every AI agent operates through a continuous cycle that mirrors how humans approach complex tasks. This loop is the fundamental architecture of agentic AI.
Observe
Perceive the environment. Read inputs, check tool outputs, review prior results.
→Think
Reason about the observation. Plan the next step. Decide which action to take.
→Act
Execute the chosen action: call a tool, write code, search the web, or respond.
→Learn
Observe the result. Update context. Determine if the goal is met or if another loop is needed.
This loop runs repeatedly until the agent determines that the goal has been achieved, it encounters an unrecoverable error, or it reaches a predefined step limit. The power of the agent lies in its ability to chain many actions together, adapting its plan based on intermediate results.
Example: You ask an agent: "Research the top 5 competitors for our new product and create a comparison spreadsheet." The agent might: (1) search the web for competitor information, (2) read several web pages, (3) extract key data points, (4) write Python code to create a spreadsheet, (5) execute the code, (6) verify the output, and (7) present the result. Each step is an iteration of the agent loop.
Types of AI Agents
Computer science has long studied different architectures for intelligent agents. These categories form a spectrum from simple to sophisticated, and modern LLM-based agents draw from all of them.
Simple Reflex Agents
Act based on the current input using predefined condition-action rules. They have no memory and no model of the world. A thermostat is a simple reflex agent: if temperature is below the threshold, turn on the heater.
Limitation: Cannot handle situations not covered by their rules. No ability to plan or learn.
Model-Based Agents
Maintain an internal model of how the world works and use it to interpret observations. They can handle partially observable environments because they track state that is not directly visible.
Example: A self-driving car that maintains a map of nearby vehicles even when they are temporarily occluded.
Goal-Based Agents
Have explicit goals and can evaluate whether their actions bring them closer to or further from those goals. They can plan sequences of actions, considering future consequences rather than just reacting to the present.
Example: A logistics agent that plans the optimal delivery route considering traffic, fuel, and time constraints.
Learning Agents
Improve their performance over time by learning from experience. They have a performance element (acts), a critic (evaluates outcomes), a learning element (improves the performance element), and a problem generator (suggests new experiences to learn from).
Example: Modern LLM agents that refine their approach based on tool execution results and user feedback within a session.
Tools and Function Calling: How Agents Interact with the World
An LLM on its own can only produce text. What transforms it into an agent is the ability to use tools -- external functions and APIs that the agent can invoke to take actions in the real world.
Tools give agents superpowers beyond language:
- Web search: Access up-to-date information beyond the training cutoff
- Code execution: Write and run Python, JavaScript, or SQL to process data and perform calculations
- File operations: Read, write, and modify files on disk
- API calls: Interact with external services -- send emails, create calendar events, query databases, deploy code
- Browser automation: Navigate websites, fill forms, extract data from web pages
Function calling is the mechanism that makes this work. The AI model is given a schema describing available tools (their names, parameters, and descriptions). When the model determines that a tool is needed, it outputs a structured function call instead of regular text. The agent framework executes the function, feeds the result back to the model, and the loop continues.
The ReAct Pattern: One of the most influential agent architectures is ReAct (Reasoning + Acting). The model explicitly alternates between reasoning steps ("I need to find the current stock price, so I should use the web search tool") and action steps (actually calling the search tool). This interleaving of thought and action makes agent behavior more transparent, debuggable, and reliable.
Multi-Agent Systems: Teams of AI
Just as complex human projects require teams with different roles, complex AI tasks benefit from multiple specialized agents working together. A multi-agent system coordinates several agents, each with distinct expertise, tools, and responsibilities.
Specialization
Each agent focuses on what it does best. A research agent searches and synthesizes information. A coding agent writes and debugs code. A review agent checks quality. Specialization produces better results than one generalist agent trying to do everything.
Communication
Agents pass information to each other through structured messages. The research agent sends its findings to the coding agent. The coding agent sends its output to the review agent. This mirrors how human teams hand off work.
Orchestration
A supervisor or orchestrator agent manages the workflow, deciding which agent to invoke next, handling errors, and ensuring the overall task stays on track. Frameworks like LangGraph, CrewAI, and AutoGen provide infrastructure for building these systems.
Emergent Behavior
Multi-agent systems can exhibit behaviors more sophisticated than any individual agent. Agents can debate, critique each other's work, propose alternatives, and converge on solutions through collaborative reasoning.