"AI agent" gets used to describe everything from a simple scripted loop to an elaborate autonomous system, which makes the term more confusing than it needs to be. Stripped down, an agent is a straightforward pattern: a model that can take an action, observe the result of that action, and decide what to do next based on that observation. Repeated in a loop until the task is done. Here's how to build the simplest genuinely useful version of that.
The core loop, conceptually
A basic agent has four parts working in a cycle: a goal (what the agent is trying to accomplish), a set of tools it can use to take actions (searching the web, reading a file, calling an API. Anything beyond just generating text), a way to observe the result of each action, and a decision step where the model looks at the goal, the history of actions and observations so far, and decides what to do next. This repeats until the model decides the goal is achieved or it hits a defined stopping condition, a maximum number of steps, most commonly, to prevent an agent from looping indefinitely on a task it can't actually complete.
Start narrower than you think you need to
The most common mistake in a first agent-building attempt is scoping the goal too broadly. "manage my email" instead of "draft a reply to this specific email using this specific template." A narrow, well-bounded task is dramatically easier to get working reliably, and it lets you actually observe and debug the agent's decision-making at each step, which is nearly impossible with a broad, open-ended goal where there's no clear definition of "done." Build the narrow version first, and only broaden scope once it's reliable.
Give it a small number of well-documented tools
Every tool you give an agent access to should have a clear, specific description of what it does and when to use it, the model decides which tool to invoke based on that description, so a vague or overlapping tool description is a common source of the agent choosing the wrong action. Start with two or three tools, not ten; a smaller, well-documented toolset is easier for the model to use correctly and easier for you to debug when something goes wrong.
Log every step, and read the logs
This is the step most beginner agent projects skip, and it's the one that matters most for actually getting something working: log the full sequence of the agent's reasoning, the action it chose, and the observation it received at every step, and read through that log when the agent doesn't do what you expected. Agent failures are almost always explainable by looking at this trace (a misleading tool description, an ambiguous instruction, a case the goal didn't anticipate) but they're nearly impossible to diagnose without it.
Build in a stopping condition and a human checkpoint
For anything beyond a toy exercise, give the agent a maximum number of steps so it can't loop indefinitely, and for any action with real consequences (sending an email, modifying a file, spending money) build in a human confirmation step before the action executes, rather than letting the agent act fully autonomously from the start. This connects directly to the verification discipline we cover in our guide to fact-checking AI-generated content: an agent's confident decision to take an action is not the same as that decision being correct, and the stakes of an autonomous action are generally higher than the stakes of a generated sentence. It's also worth setting realistic expectations: even well-built agentic tools face real failure rates in production. Gartner projects 40% of agentic AI projects will fail by 2027, covered in more depth in our piece on enterprise AI procurement, citing escalating costs and inadequate risk controls as leading causes. A tight scope and a human checkpoint are exactly the kind of risk controls that finding points to.
Where to go from here
Once a narrow, well-logged, human-checkpointed agent is reliably working, expanding its scope (more tools, less frequent human checkpoints for lower-stakes actions) is a much safer incremental process than starting broad. For the underlying prompting discipline that makes an agent's individual decision steps more reliable, see our guide to writing better prompts, which applies directly to how you write the decision-step instructions inside an agent loop.