---
title: "What Is an AI Agent? Definition, Architecture & Examples"
url: https://alicelabs.ai/en/insights/what-is-an-ai-agent
description: "An AI agent is an LLM-powered system that perceives, reasons, and acts using tools to achieve goals. Definition, architecture, examples, and comparison with chatbots."
lang: en
---

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AI Agents Definition Fresh Last reviewed: 14 September 2026 · 30d ago

# AI Agent

/ˌeɪ.aɪ ˈeɪ.dʒənt/

A software system that uses an LLM as its reasoning engine to perceive, plan, act with tools, and observe — looping until a goal is achieved.

Also known as: LLM agent · Autonomous agent · Agentic AI · Intelligent agent · AI assistant (informal)

## TL;DR

Quick Answer

Cited by AI

> An AI agent is an LLM-powered software system that perceives inputs, plans actions, calls tools (APIs, code, retrieval), and acts on results — looping until a goal is reached. Unlike a chatbot that answers single turns, an agent makes autonomous decisions across multi-step workflows.

Image: Linus Ingemarsson - Author at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png)

Written by

Linus Ingemarsson: https://www.linkedin.com/in/linus-ingemarsson/

Image: Eric Lundberg - Reviewer at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png)

Reviewed by

Eric Lundberg

Published April 15, 2026 · Updated September 14, 2026

7 min read

## In context

Customer support automation

"An AI agent reads an incoming support ticket, queries the order system, checks shipping status via the carrier API, drafts a response, and either sends it or escalates to a human reviewer."

Research and report generation

"Given a research question, an AI agent searches multiple sources, summarizes findings, generates a structured report, and cites sources — all in a single autonomous run."

Software engineering

"An AI coding agent (Cursor, Claude Code, Devin) reads a bug report, navigates the codebase, makes edits, runs the test suite, and iterates until tests pass."

Sales prospecting

"An agent enriches a lead from public sources, scores fit against ICP criteria, drafts a personalized outreach message, and adds the lead to the CRM with a recommended next action."

## Related terms

Multi-agent system: https://alicelabs.ai/en/insights/multi-agent-systems

Agentic AI: https://alicelabs.ai/en/insights/what-is-agentic-ai

LLM (Large Language Model): https://alicelabs.ai/en/insights/what-is-an-llm

Tool use / Function calling: https://alicelabs.ai/en/insights/what-is-function-calling

RAG (Retrieval-Augmented Generation): https://alicelabs.ai/en/insights/what-is-rag

## Key points

- An AI agent is defined by its loop: perceive → reason → act with tools → observe → repeat. A single-turn chatbot is not an agent.
- All modern AI agents share four components: an LLM brain, tools (functions/APIs), memory (short and long term), and a control loop (often called the orchestrator).
- The 'AI agent' term in modern usage emerged with OpenAI Function Calling (June 2023) and the AutoGPT demos earlier that year — though 'intelligent agent' as a research concept dates to the mid-1990s (Russell & Norvig).
- Agentic AI is the broader paradigm; AI agents are the concrete software artifacts that implement it.
- Anthropic's December 2024 'Building effective agents' guide makes a clear distinction between workflows (predefined steps) and agents (dynamic, model-driven decisions) — many production systems are best as workflows, not full agents.

01 / 04 Section

## The Four Components of Every AI Agent

In short

Every modern AI agent has four parts: (1) an LLM as the reasoning engine, (2) a set of tools the agent can invoke, (3) memory (short-term context and optional long-term store), and (4) a control loop that decides when to act, when to call a tool, and when to stop. Frameworks like LangGraph, CrewAI, and AutoGen all express variations of this architecture.

The four components in detail:

- **LLM brain.** The reasoning engine — typically GPT-4-class, Claude, or Gemini. Responsible for parsing the task, deciding what to do next, and producing the final response.
- **Tools.** Functions the agent can call: search a knowledge base, query an API, execute code, send an email, write a file. Defined via OpenAI Function Calling schemas, Anthropic tool use, or framework-specific equivalents.
- **Memory.** Short-term context (the conversation so far, plus tool results) lives in the LLM's context window. Long-term memory (cross-session) typically uses a vector database or key-value store.
- **Control loop.** The orchestrator that runs perceive → reason → act → observe in a loop. Frameworks differ in how explicit and controllable this loop is — LangGraph makes it a graph, CrewAI makes it role-based collaboration, AutoGen makes it conversational.

02 / 04 Section

## AI Agent vs Chatbot vs Workflow

In short

A chatbot answers single turns. A workflow executes predefined steps in a fixed order. An AI agent makes autonomous decisions about which steps to take, in what order, and when to stop — using an LLM as the decision-maker. Anthropic's 'Building effective agents' (Dec 2024) recommends using workflows when the path is predictable, and agents only when dynamic decision-making is required.

The clean distinction:

- **Chatbot.** Single-turn or multi-turn dialogue. No tool use, no autonomous action. Output is text.
- **Workflow.** A predefined chain of steps (sometimes including LLM calls). Order is fixed by the developer. The LLM does not decide what to do next; it just fills in steps.
- **Agent.** The LLM decides what to do next at each step — whether to call a tool, ask a clarifying question, or stop. Order is dynamic and model-driven.

Anthropic's December 2024 engineering guide emphasizes a pragmatic point: most production systems should be workflows, not full agents. Agents add power but also add cost, latency, and unpredictability. Reach for an agent only when the task genuinely requires dynamic decision-making.

03 / 04 Section

## Common Types of AI Agents

In short

The most common types in 2026 are: tool-use agents (single LLM + tools, the dominant pattern), ReAct agents (reasoning interleaved with acting), planner-executor agents (a planner agent decomposes the task, executor agents run the steps), multi-agent systems (multiple specialized agents collaborate), and human-in-the-loop agents (the loop pauses for human approval).

- **Tool-use agent.** The simplest and most common — one LLM, a list of tools, a control loop. OpenAI Function Calling / Anthropic tool use is the underlying primitive.
- **ReAct agent.** Reasoning and acting interleaved. The agent thinks out loud (Reason), then takes an action (Act), then observes the result, then thinks again. Introduced in the ReAct paper (Yao et al. 2022).
- **Planner-executor.** A planner agent decomposes the task into steps; one or more executor agents carry them out. Useful for long-horizon tasks.
- **Multi-agent system.** Multiple specialized agents collaborate (researcher, writer, reviewer). CrewAI and AutoGen are designed around this pattern.
- **Human-in-the-loop (HITL) agent.** The loop pauses at defined points to request human approval before taking high-risk actions. Required for many enterprise and high-risk EU AI Act use cases.

Image: Linus Ingemarsson (https://alicelabs.ai/images/linus-ingemarsson.png)
Image: Eric Lundberg (https://alicelabs.ai/images/eric-lundberg.png)

Alice Labs practitioner team

## Ready to build your first AI agent?

Alice Labs ships production AI agents — including governance, observability, and human-in-the-loop controls — for enterprises across Sweden and Europe.

Talk to an agent engineer

04 / 04 Section

## When to Use an AI Agent (and When Not To)

In short

Use an agent when the task has unpredictable structure, requires multiple tool calls, or benefits from iterative refinement. Skip it when the task is a fixed sequence (use a workflow), single-turn (use a chatbot or function call), or low-tolerance for non-determinism (use deterministic code).

The pragmatic decision rule:

- **Use an agent** when: the task path is genuinely unpredictable, the agent needs to choose among many tools, or iterative refinement (think → try → revise) produces materially better results.
- **Use a workflow** when: the steps are known in advance, even if some steps involve LLM calls. Workflows are cheaper, faster, and more predictable.
- **Use deterministic code** when: the task can be expressed as traditional logic. LLMs are not good at math, exact lookups, or strict business rules — keep those in code.

Buyers evaluating a delivery partner rather than a framework can compare our ai agents services (https://alicelabs.ai/en/ai-agents) and the pattern we ship agents on.

For a curated shortlist of vendors that operationalize these patterns, see our ai agent development companies (https://alicelabs.ai/en/insights/ai-agent-development-companies-2026) listicle.

## About the Authors & Reviewers

Written by

Image: Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png)

Linus Ingemarsson (https://www.linkedin.com/in/linus-ingemarsson/)

CEO & Co-Founder, Alice Labs

CEO & Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks.

- 8+ years in AI strategy & implementation
- Top-5 AI Speaker, Sweden (Mindley 2025)
- 100+ enterprise AI engagements

View profile: https://www.linkedin.com/in/linus-ingemarsson/

Linus Ingemarsson on LinkedIn: https://www.linkedin.com/in/linus-ingemarsson/
Email Linus Ingemarsson: linus@alicelabs.ai

Reviewed by September 14, 2026

Image: Eric Lundberg - Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png)

Eric Lundberg

Co-Founder, Alice Labs

Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations.

- AI automation & agent systems lead
- Workflow design across 100+ deployments
- Specialist in RAG, integrations & APIs

Eric Lundberg on LinkedIn: https://www.linkedin.com/in/eric-lundberg-3530451bb/
Email Eric Lundberg: eric@alicelabs.ai

Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line.

## Frequently Asked Questions

### What is an AI agent in simple terms?

An AI agent is software that uses a large language model (like GPT-4 or Claude) as its 'brain' to make decisions and take actions on your behalf — by calling APIs, searching data, writing code, or sending messages — until a goal is achieved. Unlike a chatbot, which answers one question at a time, an agent works through multi-step problems autonomously.

### What is the difference between AI and an AI agent?

'AI' is the broader field. An AI agent is a specific kind of AI system — one that acts autonomously over multiple steps using tools, not just answers a single question. A traditional image-classification model is AI but not an agent. ChatGPT used as a single-turn Q&A is AI but not an agent. ChatGPT used inside a system that calls APIs and loops on its own is an agent.

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

A chatbot answers single turns or holds a conversation, but does not autonomously take actions in the world. An agent uses an LLM to decide what to do next — call an API, query a database, send a message, write code — and runs that loop until the task is done. The defining feature of an agent is autonomous multi-step action with tools.

### Are ChatGPT and Claude AI agents?

Out of the box, the consumer chat products are mostly chatbots that occasionally use built-in tools (web search, code interpreter). ChatGPT's Custom GPTs and the Assistants API let you build proper agents on top of GPT-4. Anthropic's Claude with tool use and Computer Use likewise lets you build agents on top of Claude. The model is the engine; the agent is the system you build around it.

### What is agentic AI?

Agentic AI is the broader paradigm of building AI systems that act autonomously in pursuit of goals — using tools, making decisions, and adapting based on results. AI agents are the concrete software artifacts that implement agentic AI. The terms are often used interchangeably; 'agentic AI' is more commonly used for the strategic / capability framing, 'AI agent' for the specific software.

### How is an AI agent built?

Most production AI agents are built with a framework — the leading open-source options in 2026 are LangGraph, CrewAI, AutoGen / AG2, Semantic Kernel, LlamaIndex, and Pydantic AI. You provide an LLM (via API), define the tools the agent can call, write a system prompt, and use the framework's orchestrator to run the perceive-reason-act-observe loop.

### Are AI agents safe to use in production?

Yes, with appropriate controls. Production-safe agent design includes: scoped tool access, input/output guardrails, human-in-the-loop approval for high-risk actions, comprehensive observability, and an evaluation harness. For high-risk EU AI Act use cases (HR, credit, healthcare), additional governance — including Fundamental Rights Impact Assessment — is required.

Next in AI Agents

### Best AI Agent Frameworks 2026: 6 Compared (Open-Source)
https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026

## Further reading

- Anthropic — Building effective agents (Dec 2024) (https://www.anthropic.com/research/building-effective-agents)· anthropic.com
- OpenAI Function Calling documentation (https://platform.openai.com/docs/guides/function-calling)· platform.openai.com
- ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al. 2022) (https://arxiv.org/abs/2210.03629)· arxiv.org
- LangGraph documentation (https://langchain-ai.github.io/langgraph/)· langchain-ai.github.io

## Related services

AI Agent Development Services Alice Labs builds custom AI agents for enterprise clients.: https://alicelabs.ai/en/ai-agents

## Related reading

deep dive

### Best AI Agent Frameworks 2026

The 6 leading open-source agent frameworks compared.

11 min
https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026

quick take

### What Is Agentic AI?

The broader paradigm — agentic AI vs single-turn LLMs and Andrew Ng's four design patterns.

6 min
https://alicelabs.ai/en/insights/what-is-agentic-ai

deep dive

### AI Agent Architecture Patterns

Tool-use, ReAct, planner-executor, and multi-agent architectures with memory and planning components.

10 min
https://alicelabs.ai/en/insights/ai-agent-architecture-patterns

deep dive

### Enterprise AI Strategy: 6-Step Framework

Where agents fit in the broader AI strategy.

12 min
https://alicelabs.ai/en/insights/enterprise-ai-strategy-framework

## Sources

1. Russell & Norvig — Artificial Intelligence: A Modern Approach (4th edition, Pearson 2021) (https://aima.cs.berkeley.edu/)(accessed 2026-04-15)
2. Anthropic — Building effective agents (engineering guidance, December 2024) (https://www.anthropic.com/research/building-effective-agents)(accessed 2026-04-15)
3. OpenAI — Function calling and tool use (developer documentation) (https://platform.openai.com/docs/guides/function-calling)(accessed 2026-04-15)
4. Yao, Zhao, Yu, Du, Shafran, Narasimhan, Cao — ReAct: Synergizing Reasoning and Acting in Language Models (arXiv:2210.03629, 2022) (https://arxiv.org/abs/2210.03629)(accessed 2026-04-15)
5. LangGraph documentation (LangChain) (https://langchain-ai.github.io/langgraph/)(accessed 2026-04-15)

Next scheduled review: 2026-12-13

Alice Labs practitioner team

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