How to create an AI agent

An AI agent is a language model that takes actions, not just produces text. You give it a goal and a set of tools (functions, APIs, a browser, a file system), and it runs in a loop: decide, act, read the result, decide again, until the work is done. "How do I create one" has three honest answers depending on who you are and what you are building. The most common mistake is starting heavier than you need to.

This guide gives you that fork, a first build for each route, the failures to design for from the start, and the resources actually worth your time. If you want the mechanism in depth first, read how AI agents work. Otherwise start here.

First: do you actually need an agent?

Most tasks people reach for an agent on are better served by something simpler and more reliable. An agent earns its keep when the steps are not known in advance and the model genuinely has to decide what to do next based on what it finds along the way. When the steps are fixed, a workflow is cheaper and far more predictable. When it is a single request, a good prompt or a reusable skill is enough.

So spend two minutes on prompts vs skills vs workflows vs agents before you build anything. Choosing "agent" by default is the most expensive habit in this space. Agents are slower, they cost more per run, and they fail in more ways. Reach for one when the open-endedness is real, not because the word is everywhere.

What every agent is, underneath

Three parts, whichever route you take:

1. A model that decides the next step.
2. A set of tools it can call, each with a clear input and output.
3. A loop that runs the model, executes any tool it calls, feeds the result back, and repeats until the model signals it is finished.

Everything below is a different amount of that machinery being handled for you. A no-code platform hides all three. A framework hands you the loop and the tool plumbing. Building from scratch means you write the loop yourself, which is the fastest way to actually understand what an agent is.

Route 1 — No-code, if you do not write code (or just want it fast)

You can build a working agent without touching a programming language. This is the right starting point for non-engineers, and a genuinely good way for anyone to prototype before committing code.

What you build with: assistant-style agents inside Claude Projects or a custom GPT for simple "answer over my instructions and connected data" tasks; automation platforms like n8n, Make, and Zapier that now have AI and agent steps; and dedicated agent builders like Dify, Flowise, Lindy, and Relay. Browse the tools catalogue for current options in each category.

What you can realistically ship this way: a research assistant that searches and summarizes, an inbox or ticket triager that classifies and drafts replies, a scheduled report that gathers numbers and writes the summary.

Where it stops: you get less control over the loop, debugging a misbehaving run is harder when the platform hides the steps, per-run cost can surprise you at scale, and you are tied to one vendor's building blocks. When you hit those walls, that is the signal to move to code, not before.

Route 2 — A coding framework, if you write code

If you are comfortable in Python (or TypeScript), a framework gives you the loop and the tool wiring so you write the parts that are specific to your problem.

The honest landscape:

• An official model SDK (the Anthropic Claude Agent SDK, or the OpenAI Agents SDK) is the place to start for most single-model agents. It is light, current, and built by the people who built the model. Our agents page has copy-paste quickstarts and a decision matrix for the three Claude variants.
• LangGraph is worth it when you need explicit control over multi-step state: branching, retries, pausing for human approval, several agents coordinating. It models the agent as a graph you can reason about.
• LangChain is a large library of pre-built integrations. Useful when you want many connectors out of the box, heavier than you need for a simple agent.

The rule that saves you time: do not adopt a heavy framework before you have hit a wall that a few lines of your own code could not solve. Start with the official SDK, add a graph framework only when your control flow genuinely demands it.

A minimal tool-calling agent is short. In pseudocode:

tools = [search, fetch_page]
messages = [system_prompt, user_goal]
for step in range(MAX_STEPS):
    reply = model.respond(messages, tools)
    if reply.is_final:
        return reply.text
    for call in reply.tool_calls:
        result = run_tool(call)          # returns errors as data, never crashes
        messages.append(tool_result(call, result))

That loop, with real tools and a step cap, is a working agent. Frameworks add structure around it; they do not change the shape.

Route 3 — From scratch, to actually understand the loop

Writing that loop yourself, against the raw model API, is the best way to learn what is really happening and is often all a simple agent needs. You define the tools as plain functions, describe them to the model, run the loop, and stop on a final answer or a step cap. Reach for a framework later, when state and coordination get complex enough that hand-rolling them stops being worth it.

Your first agent, concretely

Pick the simplest route that fits, then build one small thing end to end rather than a grand system:

1. Pick a narrow goal the model cannot do in one shot. A good first project: "given a topic, search the web, read the top results, and return a sourced summary."
2. Define two or three tools. For that project: a web search, a page fetch. Each takes clear inputs and returns clear outputs.
3. Write the system prompt as a goal plus a stop condition: what done looks like, and when to give up.
4. Run the loop with a hard step cap (say, eight steps) so a confused run cannot spin forever.
5. Read the trace. Look at every decision and tool call. This is where you learn how your agent actually reasons, and where you will find most of the bugs.

Ship that, watch it run on ten real inputs, and you will understand more than any roadmap can teach.

Build for failure from day one

Agents fail in ways single prompts do not. Design for it from the first version:

• Tools fail. Networks drop, APIs return errors. Return the error to the model as data it can react to, never let a tool crash the loop.
• Loops run away. Always cap steps and total cost. A model stuck retrying the same broken call will happily burn your budget.
• Tool calls get hallucinated. Validate the arguments a model passes before you execute anything that writes data or spends money.
• Cost compounds. Every loop step is another model call. Log token use per run so a cheap-looking agent does not quietly become expensive.
• You cannot improve what you do not measure. Write a handful of test cases with expected outcomes (this is what "evals" means in practice) before you scale. Evals are how you tell whether a change made the agent better or just different.

How AI agents work goes deeper on these failure modes if you want the full picture.

Resources worth your time

A short, honest list rather than a link dump:

• Anthropic, "Building effective agents" (anthropic.com/engineering/building-effective-agents) — the clearest plain-language breakdown of when to use an agent and which pattern to reach for. Read this first.
• The official Agent SDK docs (Anthropic and OpenAI) — runnable quickstarts you can copy and adapt in an afternoon.
• Our agents page — the three Claude agent shapes with working code and a decision matrix, plus links to demo repositories.
• DeepLearning.AI short courses (deeplearning.ai/short-courses) — free, structured video walkthroughs on agentic patterns and the common frameworks, if you learn better by watching someone build.
• MCP servers — the Model Context Protocol is the emerging standard for giving an agent tools. Browsing existing servers is the fastest way to see what tools you can plug in without writing them yourself.

Where to go next

• Prompts vs skills vs workflows vs agents — confirm an agent is the right shape before you invest.
• How RAG works, and when to use it — the standard way to ground an agent in your own data.
• How AI agents work — the mechanism and failure modes in depth.
• The tools catalogue and MCP servers — the building blocks to wire in.