How to Build an AI Agent: Frameworks, Patterns and Pitfalls

By Suraj Kamdev · 2026-10-16 · 3 min read

An AI agent is a program where a language model decides which actions to take, calls tools to take them, observes the results and repeats until a goal is met. Building one is easier than ever, but the framework you pick shapes how much control and complexity you get. This guide covers the essentials and maps them to the tools in our Agent frameworks rankings.

The building blocks

Every agent, whatever the framework, has the same parts:

1. A model that can call tools (function calling). See AI APIs & models.
2. Tools: functions the model can invoke, such as searching a database, calling an API or sending an email. Each has a name, description and input schema.
3. A loop: call the model, execute any tool calls it requests, feed results back, repeat until it answers or hits a limit.
4. State or memory: the conversation, intermediate results, and sometimes long-term memory.
5. Guardrails: limits on steps, cost and which actions need human approval.

You can write this loop yourself in under a hundred lines. Frameworks earn their keep with tracing, structured state, multi-agent handoffs and production features.

Picking a framework

OpenAI Agents SDK: lightweight primitives

The OpenAI Agents SDK offers a small set of well-documented primitives: agents, tools, handoffs between agents, guardrails and built-in tracing. It is free and open source. A great starting point if you want structure without heavy abstraction.

LangGraph: maximum control

LangGraph models agents as stateful graphs: nodes are steps, edges are transitions. That takes more setup, but gives precise control over flow, retries, human-in-the-loop checkpoints and persistence. Choose it for complex, long-running or high-stakes workflows. It is open source with a paid platform option.

Vercel AI SDK: the TypeScript web default

The Vercel AI SDK is the default choice for streaming chat and agents inside TypeScript web apps. It abstracts over many model providers and makes streaming UI straightforward in Next.js and similar frameworks. Free and open source.

CrewAI: quick multi-agent "crews"

CrewAI lets you define agents with roles and goals that collaborate as a crew. It is easy to start with and good for exploring multi-agent ideas. Open source with a paid platform.

n8n: visual workflows with AI nodes

n8n is a visual workflow automation tool with AI agent nodes and hundreds of integrations. You can self-host it for free. Ideal when your "agent" is mostly connecting business tools and you prefer a canvas to code.

A sensible first build

1. Define one narrow job, such as "answer questions about our docs and open a support ticket if unsure."
2. Give it two or three tools, no more.
3. Set a maximum number of steps and a spending limit.
4. Require human approval for any action with side effects (sending, deleting, paying).
5. Log every model call and tool call. Tracing is how you debug agents.
6. Build a small test set of real tasks and re-run it whenever you change prompts or models.

Common pitfalls

• Too many tools. Models choose worse when overloaded. Start small.
• Vague tool descriptions. The description is the model's only documentation; write it carefully.
• No cost ceiling. Agents make many calls per task. Read our AI API pricing guide.
• Trusting tool output blindly. Web pages and emails can contain instructions designed to hijack your agent (prompt injection). Treat external content as data, never as commands.

If you would rather have an agent write your code than build one, see best AI coding agents.

Evaluating your agent

Agents fail in ways that are hard to spot by trying a few examples. Build a small evaluation set: 20 to 50 realistic tasks with what a good outcome looks like. Re-run it whenever you change the model, prompts or tools, and track pass rate, cost and steps per task. Even a spreadsheet works. Without this, every change is a guess.

Deploying it

Where an agent runs depends on its shape. Short request-response agents fit serverless functions; long-running or scheduled agents need a worker, queue or durable workflow. Our guide to AI agent hosting options covers the choices, and MCP explained shows how to plug standard tools into agents.

Human in the loop

The most reliable production agents are not fully autonomous. They draft, propose or prepare, and a person approves anything with consequences. Design this approval step from day one; it is far easier than adding it after something goes wrong.

FAQ

What is the easiest way to build an AI agent?

For code, the OpenAI Agents SDK or Vercel AI SDK. Without code, n8n's visual AI agent nodes.

Do I need a framework?

No. A simple loop with function calling works. Frameworks help with tracing, state and multi-agent patterns as you grow.

Which model is best for agents?

Models with strong tool-use, such as Claude models from Anthropic and OpenAI's models, are common choices. Test candidates on your own tasks.