AI
What Is an AI Agent and How Do You Build One?
A plain-English explanation of AI agents: what they are, how they differ from chatbots, the four core components every agent needs, the best frameworks for building one, and real-world use cases.
What Is an AI Agent?
An AI agent is a system that uses a large language model (LLM) to perceive its environment, make decisions, and take actions toward a goal — repeatedly, until the goal is complete. Unlike a chatbot that responds to a single message and waits, an agent breaks a complex goal into steps, executes each step using available tools, evaluates the result, and continues until it has finished the task or determined it cannot proceed.
The word autonomous is important here. A chatbot asks you a question and you tell it what to do next. An agent decides what to do next on its own based on the goal you gave it. It might search the web, read a document, write and run code, call an API, or send an email — all without asking you for every individual action.
AI agents are not a new concept in computer science, but they became practical for most developers only when large language models became capable enough to reason about multi-step problems and write reliable tool-calling code. The modern definition of an AI agent is almost always an LLM-powered system with access to a set of tools.
How AI Agents Differ from Chatbots
The most important difference between an AI agent and a chatbot is who drives the next step. A chatbot is reactive: it responds to your input and stops. You decide what happens next. An agent is proactive: you give it a goal and it decides what actions to take, in what order, to accomplish it.
A second key difference is tool use. Most chatbots output text. An agent calls tools — functions it can invoke to interact with the world. It might run a Python script, query a database, call a weather API, or book a calendar event. The LLM does not perform these actions directly; it generates the function call, the tool executes it, and the result is fed back to the LLM for the next decision.
- Chatbot: responds to one message, waits for your next input, no persistent goal
- AI Agent: given a goal, decides its own steps, calls tools, loops until complete
- Chatbot: outputs text only in most implementations
- AI Agent: executes real actions — API calls, code execution, database writes, file operations
- Chatbot: stateless between turns unless explicitly given memory
- AI Agent: maintains state across the full task execution loop
The Four Core Components of an AI Agent
Every functional AI agent — regardless of the framework or model — is built from four components: a model (the LLM that does the reasoning), tools (functions the model can call), memory (what the agent knows and remembers), and an orchestration loop (the logic that runs the model, handles tool calls, and decides when the task is complete).
Understanding these four components helps you build agents without a framework if needed, and helps you make better architectural decisions when you do use a framework. Most framework choices are really decisions about how to implement these four components.
- Model: the LLM that reasons, plans, and generates tool calls (Claude, GPT-4, Gemini, Llama)
- Tools: typed functions the model can invoke — web search, code execution, API calls, database queries
- Memory: conversation history (short-term), a vector database (long-term), or a key-value store (working memory)
- Orchestration loop: the code that calls the model, routes tool calls to their handlers, and feeds results back
How to Build a Simple AI Agent with the Claude API
The simplest possible AI agent is an orchestration loop that calls the Claude API, checks if the response contains a tool call, executes the tool if it does, feeds the result back to Claude, and repeats until Claude gives a final text response with no tool calls. This loop can be implemented in under 50 lines of TypeScript.
The example below builds a research agent that can search the web and summarize findings. It uses two tools: web_search and get_page_content. Claude decides when to call each tool and when it has enough information to give a final answer.
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
const tools = [
{
name: 'web_search',
description: 'Search the web for current information',
input_schema: {
type: 'object',
properties: { query: { type: 'string', description: 'Search query' } },
required: ['query'],
},
},
];
async function runAgent(userGoal: string) {
const messages: Anthropic.MessageParam[] = [
{ role: 'user', content: userGoal },
];
while (true) {
const response = await client.messages.create({
model: 'claude-opus-4-8',
max_tokens: 4096,
tools,
messages,
});
messages.push({ role: 'assistant', content: response.content });
if (response.stop_reason === 'end_turn') {
const text = response.content.find((b) => b.type === 'text');
return text?.text ?? '';
}
const toolResults = await Promise.all(
response.content
.filter((b) => b.type === 'tool_use')
.map(async (block) => ({
type: 'tool_result' as const,
tool_use_id: block.id,
content: await callTool(block.name, block.input),
}))
);
messages.push({ role: 'user', content: toolResults });
}
}
async function callTool(name: string, input: unknown): Promise<string> {
if (name === 'web_search') {
// integrate your search API here
return JSON.stringify({ results: [] });
}
return 'Tool not found';
}Tools and Frameworks for Building AI Agents
You do not need a framework to build an AI agent — the loop above works without one. But frameworks reduce boilerplate for common patterns: tool routing, memory management, multi-agent coordination, streaming, and observability. The right choice depends on your language preference, the complexity of the agent, and whether you need multi-agent support.
For production deployments, the most important considerations beyond the framework are observability (what is the agent doing and why) and cost control (how many tokens is it using per task). Both require logging every LLM call and tool invocation with the inputs, outputs, and latency.
- Claude Agent SDK: Anthropic first-party SDK for building agents with Claude — best for Claude-based agents
- LangChain: mature framework with the largest ecosystem of tools, integrations, and community resources
- LlamaIndex: strong for RAG and knowledge retrieval agents, good document processing support
- AutoGen: Microsoft framework for multi-agent conversations where agents can coordinate with each other
- CrewAI: role-based multi-agent framework where each agent has a defined role, goal, and set of tools
Real-World AI Agent Use Cases
The use cases where AI agents deliver the most value are tasks that are well-defined in terms of goal and success criteria, repeatable, time-consuming for a human, and involve a predictable set of information sources. Customer support escalation, research and summarization, code review, data extraction from documents, and scheduled reporting all fit this profile well.
The use cases where agents struggle are tasks that require visual judgment, tasks that depend on real-time sensor data, and tasks where the definition of success is subjective or requires human values. Agents work best when deployed with a human-in-the-loop review step for high-stakes decisions.
- Research assistant: web search + summarization + citation extraction on a given topic
- Code review agent: reads PR diffs, checks against style guides, posts structured feedback
- Data extraction: reads PDFs, extracts structured fields, writes to a database or spreadsheet
- Customer support triage: reads incoming support tickets, categorizes, drafts responses, escalates
- Scheduled reporting: pulls data from APIs, formats into a report, sends via email or Slack
Challenges and Limitations of AI Agents
The most common failure modes for AI agents are: getting stuck in reasoning loops (the model keeps calling tools without making progress), hallucinating tool inputs (the model invents parameters that do not exist), and failing to recognize when a task is impossible (agents sometimes continue trying rather than reporting failure).
Cost is also a real constraint. An agent that makes 20 LLM calls to complete a task costs 20 times more than a single call. Long context windows filled with tool call history can make each call expensive. Production agents need hard limits on the number of iterations and total token usage per task, with graceful fallback behavior when limits are reached.
FAQ
What is an AI agent?
An AI agent is a system that uses an LLM to pursue a goal autonomously. You give the agent a task, and it decides what steps to take — calling tools like web search, code execution, or API calls — and loops until the task is complete. Unlike a chatbot, an agent acts without waiting for your input at each step.
How is an AI agent different from a chatbot?
A chatbot responds to one message and waits for the next. An AI agent is given a goal and decides its own next steps, calling tools and making decisions autonomously until the goal is complete. Agents take real actions in the world; chatbots produce text responses.
What tools can an AI agent use?
Any function you define and register with the agent. Common tools include web search, code execution (running Python or JavaScript), file read and write, database queries, API calls (weather, calendar, email), and browser control. The agent decides when to call each tool and with what arguments.
What is the best framework for building AI agents?
For Claude-based agents, the Claude Agent SDK is the best starting point. LangChain is the most popular general framework with the largest ecosystem. For multi-agent systems where agents coordinate with each other, AutoGen or CrewAI are good choices. For simple single-agent use cases, a custom loop using the Anthropic SDK directly avoids framework overhead.
Can I build an AI agent without coding experience?
No-code tools like Zapier AI, Make (formerly Integromat), and n8n let you build basic agent-like workflows without writing code. For a full AI agent with custom tools, tool routing logic, and production reliability, some programming is required. The example in Section 4 is about 50 lines of TypeScript — achievable for someone with basic programming experience.
How do AI agents make decisions?
The LLM at the core of the agent reads the goal, the conversation history, the available tools, and any context provided, then generates either a tool call or a final answer. This is called the reasoning step. For complex tasks, chain-of-thought prompting encourages the model to write out its reasoning before deciding on the next action.
What is a multi-agent system?
A multi-agent system uses multiple AI agents that work together, each specializing in a subtask. For example, a research task might use one agent to search the web, a second to summarize findings, and a third to write the final report. Agents can run in parallel, check each other work, or hand off tasks in a pipeline.
Are AI agents safe to deploy in production?
With appropriate guardrails, yes. The key safeguards are: tool permissions limited to what the agent actually needs, maximum iteration limits per task, human review for irreversible actions (sending emails, deleting data, making purchases), logging every LLM call and tool invocation, and clear fallback behavior when the agent hits its limits.
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