What Is an AI Agent?

The four parts of every AI agent

Strip away the marketing and almost every AI agent is built from four parts. The first is a reasoning engine, almost always a large language model (an LLM like Claude, GPT, or Gemini), which interprets the goal and decides what to do next. On its own an LLM only produces text; the other three parts are what turn that text into action.

The second part is tools: the concrete actions the agent can take in the outside world, such as searching the web, running code, calling an API, querying a database, or editing a file. The third is memory, which lets the agent carry context across many steps so it remembers what it has already tried and what the goal was. The fourth is the control loop (sometimes called orchestration) that ties them together, feeding each action's result back to the model so it can decide the next move.

Take away the tools and you have a chatbot that can only talk. Take away the loop and you have a single function call. It is the combination — a model that reasons, tools that act, memory that persists, and a loop that repeats — that makes something an agent rather than a smarter autocomplete.

How an AI agent works: the perceive–decide–act loop

An AI agent runs a cycle, not a single pass. It perceives the current state (your prompt, a codebase, an open support ticket), reasons about the best next action using its language model, acts by invoking a tool to carry that action out, then observes the result of that action. It feeds the result back into the model and repeats — perceive, decide, act, observe — until the goal is achieved or it hits a stopping condition like a step limit or a request for human approval.

This loop is why agents can finish tasks that a one-shot model cannot. If a coding agent runs the test suite and three tests fail, it reads the failures and tries a different fix; if a research agent's first search returns nothing useful, it reformulates the query. The ability to act, see what happened, and self-correct is the defining mechanism. It is also why agents consume far more compute than a chatbot — a single task can be dozens of model calls plus tool round-trips, which is the main reason AI agents cost more to run than a simple chat exchange.

What AI agents can and can't do

Today's agents are genuinely capable within bounded tasks. They write and ship code, resolve customer-support tickets end to end, research a topic and draft a report, browse websites and fill in forms, and execute multi-step workflows across connected apps. The strongest ones run for long stretches with little supervision, recovering from errors along the way. Across the 31 verified agents in this index, that capability spans coding, sales, support, research, browser automation, trading, and general-purpose assistants.

What they cannot do is think or understand the way a person does. An agent does not have beliefs or genuine comprehension; it predicts useful next actions from patterns in its training data and the tools available to it. It does not 'learn from experience' on the fly the way a human employee does — a base model's knowledge is fixed at training time, and an agent only 'remembers' within the memory you give it for a task or session unless it is explicitly retrained or connected to a persistent store.

The practical consequences: agents can be confidently wrong (hallucinate), can be misled by a malicious web page (prompt injection), and can take an incorrect action with real consequences. That is why production agents run inside guardrails — permissions, spending limits, and human approval checkpoints for high-stakes steps.

Types of AI agents and where they're used

AI agents are usually grouped two ways. Computer-science textbooks classify them by decision-making sophistication: simple reflex agents that react to the current input, model-based agents that track internal state, goal-based and utility-based agents that plan toward objectives, and learning agents that improve over time. Most modern LLM-powered products behave like goal-based agents wrapped in a learning-capable model.

The more useful classification for buyers is by job. Coding agents like Claude Code and Cursor write software; AI SDRs and sales agents like Clay and Artisan's Ava handle outbound prospecting; customer-support agents like Intercom's Fin resolve tickets; research agents like Elicit and GPT-Researcher gather and synthesize information; and general-purpose agents like Manus and ChatGPT's agent mode tackle open-ended tasks across the web. Browse the full, verified set by job on the agent index, or by use case under our best-for-X guides.

Pricing varies enormously by type. Coding agents start around the individual-developer band of $25/month or less, while AI sales agents are typically custom-priced platforms running $750 to $5,000+/month, because they aim to replace payroll rather than augment a single person.

AI agent vs chatbot, assistant, and automation

The fastest way to understand what an AI agent is, is to see what it is not. A chatbot converses but does not act on the outside world; an AI agent takes real actions toward a goal. A virtual assistant handles single commands ('set a timer'); an agent plans and chains many steps autonomously. Workflow automation and RPA follow fixed, pre-defined paths; an agent decides its own path and adapts when conditions change.

Agentic AI is the broader paradigm: the design approach for building systems that act with agency, often by coordinating several agents. In that framing, an AI agent is the building block and agentic AI is the architecture assembled from those blocks. If you want the precise lines, see how an agent differs from a chatbot and how an AI agent relates to agentic AI.

What is an AI agent in simple terms?

An AI agent is software that pursues a goal on its own. You give it an objective in plain language, and it decides the steps, uses tools like a browser, code, or APIs to carry them out, checks the results, and repeats until the task is done. The key trait is that it acts, not just answers.

How does an AI agent work?

An AI agent runs a loop: it perceives the current state, reasons about the next action with a language model, acts by calling a tool, then observes the result and repeats. This perceive–decide–act cycle lets it self-correct — rerunning a failed test or reformulating a search — until it reaches the goal or a set limit.

What are the main components of an AI agent?

Four parts: a reasoning engine (a large language model) that decides what to do; tools that let it act, such as web search, code execution, or APIs; memory that carries context across steps; and a control loop that feeds each result back to the model. Remove the tools and you have a chatbot, not an agent.

What is the difference between an AI agent and a chatbot?

A chatbot answers messages inside a conversation and stops. An AI agent takes real actions toward a goal — looking up an order and issuing the refund, not just describing the returns policy — by planning, calling tools, and working through multiple steps. Action and autonomy are the dividing line between the two.

Can AI agents think independently?

Not in the human sense. An AI agent has no beliefs or genuine understanding; it predicts useful next actions from patterns in its training data and the tools it has. It can choose its own steps toward a goal, which looks like independent thinking, but it is statistical pattern-matching, not consciousness or comprehension.

Can AI agents learn from experience?

Only in limited ways. A base model's knowledge is fixed at training time, so an agent does not improve on the job like a human employee. It can 'remember' within the memory you give it for a task or session, and developers can retrain models or add persistent stores, but it does not autonomously learn from each run.

What problems do AI agents solve?

AI agents take on multi-step work that used to require a person: writing and shipping code, resolving support tickets end to end, researching and drafting reports, browsing and filling forms, and running workflows across connected apps. The win is delegating an outcome instead of doing every step yourself, freeing people for higher-value work.

How much does an AI agent cost?

It depends on the type. Across the 31 verified agents in this index, coding and individual-developer agents start around $25/month or less, while AI sales agents are typically custom-priced platforms running $750 to $5,000+/month. Open-source agents can be self-hosted for only the model API usage they consume.