AI agents have become one of the most talked-about topics in technology, and one of the most confusing to navigate. Unlike a regular AI that answers questions, an agent takes actions: it can browse the web, send emails, write and run code, and connect to the tools you already use, without you managing each step.
The honest answer to “which one should I use?” is that it depends on what you are trying to do. The tools that exist today operate at different levels, target different users, and make different tradeoffs. This guide focuses on three practical situations: getting started as an individual, automating repetitive workflows, and building something more serious for a team or business.
Best agents for individual use
For most people starting out, the choice comes down to two tools: Claude Code, built by Anthropic, and Codex, built by OpenAI. Both run inside VS Code, both can read your files, send emails, browse the web, and connect to external tools. At a surface level, they do the same job.
The practical differences emerge when you use them regularly. Codex is more token-efficient, meaning it produces comparable results while consuming less of your usage allowance, which allows you to get more done before hitting the limits of a standard subscription. For everyday tasks and terminal-based workflows, this makes it the better value at the entry price point. Claude Code, on the other hand, handles more complex tasks better: multi-file projects, complex reasoning, and anything that involves interacting with a browser or external interface as part of the task. It is also available as a VS Code extension, a desktop app, and on the web, which makes it more accessible if you prefer not to work in a terminal.
For a non-technical user trying agents for the first time, Claude Code’s interfaces and guided experience make it the easier starting point. For someone comfortable in the terminal who wants to get more volume out of a $20 subscription, Codex is more money efficient. Both are capable enough that the difference in day-to-day use is smaller than most comparisons suggest. A growing number of people use both deliberately, requesting one to do the work, the other to review it, compensating for each other’s blind spots in the process.
Best tools for process automation
Once you move beyond individual tasks and want to automate entire workflows (sequences of steps that run on their own, triggered by an event, without you initiating each one), you need a different category of tool. This is where platforms like Zapier, Make, and n8n come in.
All three let you connect your existing tools and build automated sequences between them: when a new email arrives, create a task; when a form is submitted, update a spreadsheet and notify your team. The difference between them is not what they can automate, but how difficult it is to use them.
Zapier is the most accessible. It has the largest library of app integrations and a straightforward interface designed for non-technical users. If you want something working quickly and your workflows are relatively simple, Zapier is the fastest path. The tradeoff is cost: it charges per task, which makes it significantly more expensive as volume grows.
Make sits in the middle. It offers more complex workflow logic than Zapier, such as conditional branching, data transformation, multi-step sequences, while remaining approachable without a developer. It also costs considerably less at equivalent volumes, making it the better fit for teams that have outgrown Zapier’s simplicity but don’t have engineering resources.
n8n is built for technical teams. It is open-source, can be self-hosted on your own infrastructure, and offers the deepest integration with AI tools of the three. For organizations with data privacy requirements, high automation volumes, or complex custom logic, it is the most powerful and most cost-effective option at scale, but it requires someone who can set it up and maintain it.
The right choice follows a simple logic: start with your team’s technical level, not the tool’s feature list. For most people, one of the options above will be enough. For some, it won’t.
When off-the-shelf tools aren’t enough
The tools covered so far work well for a wide range of situations. But they are built for general use, which means they come with general limitations: predefined integrations, fixed logic, and no ability to adapt to how a specific business actually operates.
For individuals and small teams, this is rarely a problem. For businesses with more complex requirements – multiple systems that need to connect, sensitive data that can’t flow through third-party infrastructure, or processes where a mistake carries real consequences – off-the-shelf tools start to show their limits.
Custom agents are built for a specific operation rather than a general one. They take more time and expertise to set up, but the result fits your processes rather than the other way around. For straightforward needs, the tools above are the right starting point. For anything more complex, the implementation matters as much as the technology itself, and that might mean getting help from outside.