Agent (topic)

An AI agent is a system that can work toward a goal on its own. The simplest way to picture it is a folder with three things inside: a model (the AI brain), tools (things like web search or file readers), and a loop (the process of deciding, acting, and checking results). One researcher found over 200 definitions, but the core idea is simple. You give an agent a goal. It decides what to do next, calls a tool, looks at the result, and keeps going until the task is done.

This matters for AI development because it changes how we use AI. A regular chatbot is like a meeting — you ask a question and get an answer. An agent is like an employee — you tell it what you want done, and it runs the full workflow. Think of a vending machine versus a slot machine. A vending machine is deterministic (same input, same output every time). An agent is non-deterministic — sometimes it wins, sometimes it loses, and it adapts as it goes.

Developers are moving away from supervising every step. Instead, they give guidance and let the agent handle the loop. An agent can read text, code, images, or PDFs, then work through tools instead of just producing chat. Understanding this shift — from simple chat to goal-directed autonomy — is one of the most important skills to learn in AI development today.

To use an agent (an AI that performs tasks autonomously), start by defining its purpose. An agent is essentially a folder with three things: instructions, context, and tools. Begin with the core instruction—a plain-language description of what the agent should do and when to use it. In Claude, you can "generate with Claude" by typing something like "Create me a sub agent that criticizes all of my work." For the best results, be comprehensive in your description. The system prompt (the main instruction file) goes into a file named `claude.md` (if using Claude Code) or `agents.md` (if using Codex). This file holds the agent's behavioral rules.

Next, add context. Include an examples subfolder with previous winning proposals for the AI to reference. This helps the agent understand the quality and style you expect. Then, define tools—the functions the agent can call, like search or data retrieval. The agent works by looping through reasoning and acting: it thinks, checks the user input, decides if it should use a tool, acts, then cycles until finished.

To test your agent, use evals (evaluations that measure performance). For example, check if the agent follows business guidelines and uses appropriate tone and vocabulary. Evals become especially important when your agent has multiple steps or uses tools like search. They help catch when the agent goes off the rails. Start by applying your agent to a recurring task you do often—that's where agents are most useful.

When building an AI agent (a model using tools in a loop), the most common pitfall is adding too much complexity. As one expert warns, "every single thing you add to an agent risks making it worse." Large system prompts and too many edge cases often degrade performance rather than improve it.

The first best practice is to start simple. A basic agent needs three things: instructions, tools it can call, and the ability to run in a loop. Apply agents to recurring tasks first, not one-off experiments. Once you have a working agent, create evals (evaluations that test agent behavior) using specific examples that represent hard cases your agent must handle. Run the agent against these evals to identify exactly where it fails.

Break complex systems into smaller, domain-specific agents. When you find one agent trying to do too much, split it into separate subject matter experts. This reduces the "vectors for failure" and makes each agent more reliable. Use verifiers to check agent outputs, especially looking for disagreement between the agent and its reviewers. Have a subject matter expert tune the review process when conflicts arise.

A common failure mode is the "cascading failure" where an agent makes an early mistake that goes unnoticed because the system runs autonomously. To prevent this, design your attention like a system—be intentional about where you enter, what you require, and what you reuse. Also watch for agents that "don't do enough leg work" on a step, like failing to ask clarifying questions or explore a codebase thoroughly before proceeding.