AI Agents: What They Are and How They Work

The key to an agent is the loop: observe (receive information), plan (decide what to do), act (execute actions with tools) and evaluate (verify if the goal was achieved). If not, start again. This cycle is what separates an agent from any other AI application.

In this guide you'll understand exactly how that loop works, what components an agent needs, when it makes sense to use them (and when not), and how to start building your own.

Agent vs chatbot

A chatbot is reactive: question-answer, question-answer. It has no initiative and no tools. Its knowledge ends where its context window ends.

An agent is proactive: it receives a goal, breaks the task into steps, executes each step using tools, and verifies the result. If something fails, it replans and retries. It can interact with the real world: read files, call APIs, modify databases, send notifications.

The fundamental difference isn't the model's intelligence, but the architecture. A chatbot uses the same model as an agent. The difference is that the agent has an execution loop, tools and the ability to act on the environment.

The 4 components of an agent

Every agent, from the simplest to an enterprise multi-agent system, has these four components:

1. LLM (brain): the language model that reasons, plans and decides. Can be GPT-4o, Claude Sonnet, Llama or any LLM. The model determines reasoning quality, but isn't the only factor. An excellent model with poorly designed tools produces mediocre results. To choose a model, check which LLM to choose.

In practice, the LLM receives the user's goal, the history of previous actions, tool results and its system prompt. With that information it decides the next step. The most recent models (Claude Sonnet 4, GPT-4o, Qwen3) are significantly better at "tool use" than versions from a year ago.

2. Tools: functions the agent can execute. Examples: search the internet, read a file, run SQL, send an email, call an API. Without tools, an agent is just a chatbot. Tools are defined with a name, description and parameter schema. The LLM decides when and how to call each tool based on the description.

Well-designed tools have clear names (search_web, read_file, send_email), precise descriptions of when to use them, and parameters with validation. To dive deeper into how they connect, read about MCP and its tools.

3. Memory: the agent needs to remember what it's done and what it's learned. There are three types of memory:

• Working memory: the current conversation context. What the agent "has in mind" right now.
• Short-term memory: tool results, previous decisions in the session. Lost when it ends.
• Long-term memory: databases, files, vector stores. Persists between sessions. To see this in practice, read about Claude Code memory.

4. Planning: the ability to break down a complex goal into executable steps. Main techniques:

• ReAct (Reasoning + Acting): the agent reasons out loud before each action. "I need to search the current price. I'll use search_web with the query..."
• Plan-and-Execute: first generates a complete plan, then executes step by step. Better for long tasks.
• Tree of Thought: explores multiple paths and picks the best. More expensive, more precise.

The agent loop step by step

Every agent follows the same fundamental cycle, regardless of complexity. Let's break it down:

Step 1: Observe. The agent receives information. It can be the user's initial goal, the result of a tool just executed, an error that needs handling, or new environment information. All this information becomes context for the next decision.

Step 2: Plan. The LLM analyzes the current situation and decides what to do. This includes: evaluating what information it has, what's missing, what tools are available, and what's the most logical next step toward the goal. In frameworks like LangGraph, this step is explicit (a "planner" node). In simpler agents like ReAct, it's implicit in the model's reasoning.

Step 3: Act. The agent executes the decided action. Usually a tool call: web search, read a file, execute code, send a message. The action produces a result (success with data, or error with failure information).

Step 4: Evaluate. The agent examines the result. Questions resolved in this step: "Did I get what I needed?", "Are there errors?", "Have I completed the goal?", "Do I need more information?". If the goal is met, the agent finishes. If not, it returns to step 1 with new information.

Types of agents

Simple agents (single agent): one LLM with tools executing a task. Example: Claude Code working on your repository. Read what is Claude Code to see a real agent in action.

Multi-agent: several specialized agents collaborating. A coordinator assigns tasks to specialized workers. Example: a research agent searches data, another analyzes it, another generates the report. Read about Claude Code subagents for a concrete implementation.

Autonomous agents: agents operating without continuous human supervision. They monitor, detect events and act. Example: a SOC agent analyzing security alerts 24/7. Read about Claude managed agents for a concrete example.

Stateful agents: maintain a state graph that determines which transitions are possible. LangGraph is the reference framework for this pattern. Each node is a function, each edge is a condition. The agent can't "skip" steps defined in the graph.

Agents in 2026: state of the art

As of May 2026, AI agents have advanced significantly from the first attempts in 2023 (AutoGPT, BabyAGI). But it's worth separating what works from what's marketing.

What works well today:

• Code agents: Claude Code, Cursor, Copilot Workspace. Can implement complete features, fix bugs, write tests. High reliability in well-documented repos.
• Research agents: Deep Research (OpenAI, Gemini). Search, synthesize and generate quality reports. Useful for due diligence, market analysis, literature review.
• Scoped automation agents: repetitive tasks with clear scope. Email classification, document processing, periodic report generation. Implementable with n8n or similar.
• Data analysis agents: receive a question, write SQL or Python, analyze results. Work well when the database is well-documented.

What still doesn't work reliably:

• Fully autonomous agents: the idea of "give it a goal and leave it alone for hours" produces inconsistent results. The best current agents need human checkpoints.
• General web browsing agents: navigating arbitrary sites, filling forms, handling CAPTCHAs. Improving fast, but not reliable for production.
• Agents coordinating other agents without supervision: complex multi-agent systems (10+ agents) still require careful engineering and constant monitoring.

Key trend: the market is moving toward specialized agents with reduced scope and high reliability, rather than generalist agents trying to do everything. An agent that does one thing well is more valuable than one that does ten things poorly.

Practical examples by sector

Software development: receives a bug report, reads the relevant code, identifies the cause, proposes and applies a fix, runs tests. Claude Code is exactly this. Read its main commands.

Research: receives a topic, searches 10 sources, extracts relevant data, synthesizes a report with citations. Tools: web search, web scraper, LLM for synthesis.

Data analysis: receives a business question ("which are the 10 customers with highest churn risk?"), writes SQL queries, analyzes results, generates visualizations. Read about data analysis prompts.

Marketing: monitors brand mentions, analyzes sentiment, identifies response opportunities, drafts content for approval. Integrates social media automation tools with LLM analysis.

Legal: reviews contracts against a mandatory clause checklist, identifies risks, suggests modifications, generates a compliance report. The agent doesn't replace the lawyer, but reduces initial review from 4 hours to 20 minutes.

HR: filters CVs against job requirements, generates personalized interview questions, schedules interviewer calendars. Tools: document parser, candidate database, calendar API.

Email automation: monitors incoming emails, classifies by urgency, automatically responds to standard ones, escalates complex ones to the right person. Implementable with email automation with AI.

When NOT to use an agent

Not everything needs an agent. In fact, most AI tasks are better solved without one. An agent introduces complexity, cost and failure points. Use it only when the benefit justifies that complexity.

Use a simple prompt when:

• The task is transformation (summarize, translate, reformat). Learn to do it well with the prompt chaining guide.
• You don't need information external to the context you already have.
• The output is predictable and doesn't require iteration.

Use traditional automation (n8n, Zapier) when:

• Steps are fixed and predictable (always the same, in the same order).
• You don't need "reasoning" at each step, just execution.
• Reliability is more important than flexibility. Read the Zapier vs Make vs n8n comparison.

Use an agent when:

• Steps depend on previous results (you can't predict the path in advance).
• You need the system to make context-based decisions.
• The task requires exploration, retry, or adaptation to failures.
• Input is variable and each execution may take a different path.

Security and permissions in agents

An agent with access to your email, database and terminal is powerful. And dangerous if something goes wrong. Security in agents isn't optional: it's what separates a prototype from a production system.

Human-In-The-Loop (HITL): for irreversible or high-impact actions, the agent must request human approval before executing. Examples: deleting data, sending emails to customers, executing payments, modifying production configuration. Claude Code implements this well: it asks permission before executing commands that modify the system.

Granular permissions: don't give full access. Define exactly which tools each agent can use, with which parameters, and in which contexts. A data analysis agent doesn't need access to email-sending tools. Each tool should have an associated risk level.

Sandboxing: run agents in isolated environments. If an agent executes code, it should be in a container without network access or access to other systems. If it fails or behaves unexpectedly, the damage is contained.

Logging and auditing: record every agent action. Which tool it called, with which parameters, what result it got, what decision it made. This is essential for debugging, compliance, and understanding what happened when something goes wrong.

Tools for building agents

No-code: Claude Code (development agent), ChatGPT GPTs (custom agents), Zapier Central (automation agents).

Low-code: n8n (workflows with AI Agent nodes), Flowise, Dify. Drag components and connect. Ideal for business automations without writing code.

With code: LangGraph (LangChain agent framework, read the LangChain tutorial), CrewAI (declarative multi-agent), AutoGen (Microsoft), Anthropic Agent SDK (Python, for building on Claude).

Connectivity: MCP (Model Context Protocol) is the open standard for connecting agents with external tools. Any tool implementing MCP works with any compatible agent. Read about MCP and its tools.

Current limitations

Hallucinations: an agent that hallucinates doesn't just say nonsense, it executes nonsense. An agent that deletes the wrong files is worse than one that does nothing. Mitigation: tools with validation, confirmation before destructive actions, and automated tests after changes.

Cost: each loop iteration consumes tokens. A complex agent can cost 1-5 USD per execution. Multiplied by thousands of executions, costs scale. Optimization: use cheaper models for simple decisions, cache tool results, limit iterations.

Reliability: agents aren't 100% reliable. For critical tasks, you need human supervision (HITL) for important decisions. Reliability improves dramatically with reduced scope, well-defined tools, and clear system prompts.

Latency: each loop iteration involves an LLM call (1-5 seconds) plus tool execution. An agent needing 10 iterations can take 30-60 seconds. For interactive tasks, this may be too much.

Debugging: when an agent fails, understanding why is difficult. The model's reasoning is opaque, decisions depend on accumulated context, and reproducing the same behavior isn't guaranteed. Detailed logs are your best tool.

FAQ

Next step

The fastest way to understand agents is to use one. Install Claude Code and ask it to do something in your project. Watch how it plans, executes and verifies. That's an agent in action.

If you prefer building yours from scratch, the LangChain tutorial gives you the basics. If you want no-code automation, start with the n8n tutorial. And if you want to better understand the prompts that power agents, the system prompts guide is your next read.