What Are AI Agents?

An AI agent is an autonomous system that perceives its environment and takes actions to achieve goals. In the broadest sense, this definition encompasses everything from a simple thermostat to a self-driving car. This hub focuses specifically on LLM-powered agents, where a large language model serves as the core reasoning engine — interpreting observations, deciding on next steps, and determining when a task is complete. Unlike a traditional chatbot that simply responds to prompts in a single turn, an LLM-powered agent operates in a loop — continuously observing outcomes, reasoning about what to do next, and acting until its objective is fulfilled.

The simplest way to understand the distinction: a chatbot answers questions; an agent accomplishes tasks. When you ask a chatbot "What is the weather in Tokyo?", it gives you a text answer. When you ask an agent the same question, it calls a weather API, parses the result, and may even follow up by suggesting you pack an umbrella if rain is forecast.

More formally, an AI agent exhibits these core properties:

• Autonomy — It operates without constant human intervention, making its own decisions about which actions to take.
• Goal-directed behavior — It works toward completing a specific objective rather than merely generating text.
• Tool use — It can invoke external tools, APIs, and services to interact with the real world.
• Perception — It senses the environment through various inputs — user messages, tool results, error messages, and external data — and incorporates this information into its decisions.
• Persistence — In production systems, agents typically maintain state across multiple steps, remembering what they have already done and learned. This may range from short-term context within a single session to long-term memory persisted across sessions.

Every AI agent, regardless of framework or architecture, operates on a fundamental cycle often called the agent loop. This loop has four core phases that repeat until the task is complete:

1. Perceive — The agent observes its current state. This includes the user's original request, the results of any previous actions, error messages, and any new information from the environment. All of this is assembled into the LLM's context window.
2. Reason — The LLM processes the accumulated context and decides what to do next. This is where strategies like Chain of Thought (CoT) or ReAct come into play. The model may think step-by-step, weigh alternatives, or plan several moves ahead.
3. Act — The agent executes a concrete action: calling a tool, writing to a database, sending an API request, or generating a final response to the user. The result of this action feeds back into the Perceive step, and the loop continues.
4. Memory — The agent stores important results and context in memory (short-term context window and optionally long-term storage) so it can reference past observations and actions in future iterations. Memory is what allows agents to build on previous steps rather than starting from scratch each time.

Think of a chef preparing a complex dish: they look at the recipe and ingredients (Perceive), decide what to cook next (Reason), chop vegetables or adjust heat (Act), and remember what they already prepared so they do not repeat it (Memory). Each iteration brings the dish closer to completion.

goal = get_user_request()
memory = Memory()
task_complete = False

while not task_complete:
    observation = perceive(environment, memory)
    thought     = reason(observation, goal)
    result      = act(thought)
    memory.store(result)          # 4th phase: persist for future steps
    if is_final_answer(result):
        task_complete = True

This loop is deceptively simple, but it is the foundation of all agentic behavior. The quality of an agent is determined by how well it performs each phase — how accurately it perceives, how intelligently it reasons, how reliably it acts, and how effectively it uses memory.

The classic taxonomy of AI agents, introduced by Russell and Norvig in Artificial Intelligence: A Modern Approach, identifies five types: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. For practical LLM agent development, this spectrum maps neatly onto a simpler reactive/deliberative/hybrid framing that emphasizes how much planning an agent performs before acting.

AI agents can be categorized by how they balance reactive behavior (responding to immediate inputs) with deliberative behavior (planning ahead). The three main types are:

Reactive Agents

Reactive agents respond directly to stimuli without maintaining an internal model of the world. They follow simple condition-action rules: if the user asks X, do Y. Most basic chatbot wrappers with tool calling fall into this category. They are fast and predictable, but struggle with complex, multi-step tasks.

Deliberative Agents

Deliberative agents maintain an internal model of the world and plan their actions before executing them. They create explicit plans, consider alternatives, and can reason about the consequences of their actions multiple steps ahead. Plan-and-Execute and Tree of Thought agents are examples. They handle complex tasks well but are slower and consume more tokens.

Hybrid Agents

Most production agents are hybrids. They use reactive behavior for simple, well-defined tasks (quick tool calls, straightforward lookups) and switch to deliberative reasoning when they encounter complex problems that require multi-step planning. The ReAct pattern is the most widely used reasoning pattern for agents — it interleaves reasoning traces with actions in a tight loop.

Learning Agents

A fourth category worth noting is learning agents — agents that improve their behavior over time. Through techniques like Reflexion, an agent can evaluate its own past performance, identify mistakes, and adjust its strategy for future tasks. While still an emerging area, learning agents represent the frontier of agentic AI, blending elements of all three types above with self-improvement capabilities.

Choosing the right type depends on your use case. For a customer support bot that answers FAQs and files tickets, a reactive agent with a few tools is sufficient. For a research assistant that synthesizes information from dozens of sources and writes a report, you need deliberative planning.

Not every AI application needs an agent. In fact, adding agentic behavior when it is not needed introduces unnecessary complexity, latency, and cost. Here is a practical decision framework:

Use a simple LLM call when:

• The task can be completed in a single turn (summarization, classification, translation).
• No external data or tools are needed beyond what is in the prompt.
• Determinism and speed are more important than flexibility.
• The output format is well-defined and does not require iterative refinement.

Use an agent when:

• The task requires multiple steps that depend on intermediate results.
• External tools, APIs, or databases need to be queried dynamically.
• The task is open-ended and the exact steps cannot be predetermined.
• Error recovery and adaptive behavior are required.
• The user expects the system to "figure it out" rather than follow a rigid script.

A good rule of thumb: if you can write a deterministic script to solve the problem, you do not need an agent. If the solution path depends on dynamic information discovered along the way, an agent is the right choice.

AI agents are already powering production systems across industries. Here are concrete examples that illustrate the spectrum of agent capabilities:

• Coding assistants (Claude Code, GitHub Copilot agent mode, Cursor) — These agents read your codebase, plan changes across multiple files, execute code, run tests, and iterate until the task is complete. They use tools for file I/O, terminal execution, and search.
• Customer support agents — They look up order information, check knowledge bases, apply discount codes, and escalate to humans when needed. Each customer interaction may require 5-15 tool calls orchestrated by the agent loop.
• Research agents — Given a research question, they search the web, read papers, extract relevant information, synthesize findings, and produce a structured report with citations.
• Data analysis agents — They connect to databases, write and execute SQL queries, generate visualizations, and narrate insights — adapting their analysis strategy based on what the data reveals.
• DevOps agents — They monitor system health, diagnose issues from logs and metrics, propose fixes, and can even execute remediation steps with human approval.

What all these examples share is the agent loop: they perceive their environment, reason about the next step, act using tools, and iterate until the job is done. The specific tools and reasoning strategies differ, but the fundamental pattern is the same.