What actually makes an LLM an agent?

An agent is an LLM placed inside a loop where it can choose to call tools, observe the results, and decide its next step rather than answering in one shot. The model supplies the reasoning and tool choices; your code supplies the execution and the loop that ties them together.

What is the ReAct pattern?

ReAct stands for Reasoning and Acting. The model interleaves short reasoning steps with actions, so each turn it either thinks, calls a tool, or gives a final answer. Observing the tool result before the next thought lets it self-correct, which makes multi-step tasks much more reliable.

How do I stop an agent from looping forever?

Set a hard maximum number of steps and break the loop when it is reached. Also detect repeated identical tool calls, return a clear error to the model when a tool fails, and provide a final-answer tool so the model has an explicit way to finish.

How do I keep tool execution safe?

Treat every tool input as untrusted. Validate arguments against the schema, run code in a sandbox or container with no network or filesystem access by default, scope API keys to least privilege, and require a human confirmation step before any action that spends money or changes data.

Do I need a framework like LangChain to build an agent?

No. The core loop is small enough to write directly against a model's native tool-use or function-calling API. Frameworks add conveniences like prebuilt tools, memory stores, and tracing, but understanding the raw loop first makes those abstractions far easier to debug.

How to Build an AI Agent from Scratch

What an AI agent really is

An AI agent is a large language model wrapped in a loop that lets it act, not just answer. You give the model a set of tools — a web search, a calculator, an API call — and on each turn it decides whether to reason, call a tool, or finish. Your code executes the chosen tool, feeds the result back, and the loop continues. This reason-act-observe cycle is what turns a static chat model into something that can complete multi-step tasks.

How the loop works

The pattern, often called ReAct (Reasoning + Acting), is simple. You send the user’s goal along with the available tool definitions. The model responds either with a tool call or a final answer. If it calls a tool, you run it, append the result as an observation, and send the conversation back. The model uses that new information to plan its next move. The loop ends when the model emits a final answer or you hit a step cap.

The simulator below lets you walk a sample task through this loop step by step, watching the model reason, pick a tool, receive an observation, and converge on an answer — exactly the trace your own agent would produce.

Tips and guardrails

Keep tools small and single-purpose with clear schemas — vague tools confuse the model. Always set a maximum step count to prevent runaway loops, and return structured errors to the model when a tool fails so it can recover instead of guessing. Sandbox any code execution and scope every credential to least privilege. For actions that spend money or mutate data, insert a human confirmation step. Finally, log every reason-act-observe turn so you can debug why an agent went off course.