AI Agents Explained:
What They Are and How They Work

For most of its existence, using an AI model has worked like this: you send a message, you get a response. One turn. The model answers your question and then waits for the next one. It does not take actions, it does not follow up, and it certainly does not open a browser or run a command on your behalf.

AI agents change that. An agent is an AI system that can plan a sequence of steps, use external tools, observe the results, and decide what to do next — all without you telling it exactly how at each stage.

Chat vs Agent: What Is the Difference?

A regular chatbot is reactive. You ask, it answers. An agent is proactive. You give it a goal, and it figures out the steps.

How Agents Work

At a high level, every agent follows the same loop:

1. Receive a goal. "Find all the failing tests in this codebase and fix them." The user defines what success looks like, not how to get there.
2. Plan. The model breaks the goal into steps. "First, I need to find the test files. Then run the test suite. Then read the failures. Then edit the code."
3. Act. The agent calls a tool — runs a shell command, reads a file, makes an API request. This is the key difference from a chatbot. It does not just talk about what it would do; it does it.
4. Observe. The tool returns a result. The agent reads it, updates its understanding of the situation, and decides the next step.
5. Repeat. Steps 2–4 loop until the goal is achieved or the agent determines it cannot proceed.

What Tools Can Agents Use?

The power of an agent depends entirely on the tools it has access to. Common ones include:

• Web search. Look up current information the model was not trained on.
• Code execution. Write and run Python, JavaScript, or shell commands.
• File system access. Read, write, and edit files on disk.
• API calls. Interact with external services — databases, calendars, messaging platforms.
• Browser control. Navigate websites, fill forms, extract data.

Each tool is defined with a description and a schema. The model decides which tool to call and what arguments to pass, based on the current state of its plan.

Real-World Agent Examples

• Coding agents (Claude Code, GitHub Copilot, Cursor) — read your codebase, make changes, run tests, fix failures, commit.
• Research agents (Perplexity, ChatGPT with browsing) — search the web, read multiple sources, synthesise a summary.
• Customer support agents — look up a customer's account, check order status, process refunds, all through API integrations.
• Data analysis agents — read a spreadsheet, write analysis code, run it, generate charts, present findings.

The Challenges

Agents are powerful but not without problems:

• Error compounding. A wrong decision early in the plan can cascade. The agent confidently pursues the wrong path for 15 steps before anyone notices.
• Cost. Each tool call is another API request. A complex agent task might involve 50+ model calls — that adds up fast.
• Safety. An agent with file system access and shell commands can delete things. Guardrails and human-in-the-loop approval are essential.
• Reliability. Agents fail more often than simple chat. The plan might be good, but a tool returns an unexpected result and the agent does not recover gracefully.

Which Models Are Best for Agents?

Not all models are equally good at agentic tasks. The key capabilities are:

• Tool use. The model needs to reliably generate correctly-formatted tool calls.
• Planning. The model needs to break complex goals into logical steps.
• Error recovery. When a tool call fails, the model needs to diagnose the issue and try an alternative.
• Long context. Agent loops generate a lot of text — the conversation between the agent and its tools can easily exceed 100K tokens.

SWE-bench is the most relevant benchmark here — it measures whether a model can autonomously fix real GitHub issues. The top performers on SWE-bench are generally the best agent models.