# AI agent

> An AI agent is a language model placed in a loop where it can decide what to do next, call tools to do it, observe the result and continue until a goal is…

Source: https://northerngo.com/glossary/ai-agent/
Language: en
Updated: 2026-08-28

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**An AI agent is a language model placed in a loop where it can decide what to do next, call tools to do it, observe the result and continue until a goal is reached. The distinguishing feature is that the sequence of steps is chosen by the model, not fixed by the developer.**

### The parts

Four things have to be present. A **goal** stated in text. A set of **tools** — functions the model may call, described well enough that it can choose between them: search, read a file, run a query, send a request. A **loop** that feeds each tool result back in so the model can decide the next move. And a **stopping condition**, whether that is a success check, a step limit or a budget.

A common and more predictable variant is a **pipeline**: several specialised model calls in a fixed sequence, each with a narrow job. NorthernGo builds this way on the cloud path — an Architect that plans the structure, a Builder that writes the code, and a Critic that can attempt a repair of mechanical damage. Locally the Builder runs on your GPU and a syntax check blocks JavaScript that will not parse; there is no LLM rewrite. The order is fixed, which is exactly what makes the behaviour reproducible.

The distinction is worth keeping straight, because "agent" is used for both:

1. **Fixed pipeline** — developer chooses the steps. Predictable, testable, limited.
2. **Autonomous loop** — model chooses the steps. Flexible, capable of surprising you in both directions.

### What to watch out for

**A chatbot with a good system prompt is not an agent.** If it cannot take an action in the world, it is a conversation. Most products labelled "AI agent" are one model call with a nice interface.

The real engineering problem is **compounding error**. A step that succeeds 95% of the time succeeds about 60% of the time over ten steps. Long autonomous chains fail in ways that are hard to see, because step four went subtly wrong and steps five through nine confidently built on it. This is also where a [hallucination](/glossary/hallucination/) stops being a wrong sentence and becomes a wrong action.

Practical consequences:

- **Keep chains short**, verify between steps, and prefer a fixed sequence wherever the sequence is actually known.
- **Cap everything.** Step count, wall-clock time, token spend. Loops that call themselves are how you get an expensive surprise.
- **Require confirmation for anything irreversible** — deleting data, sending email, moving money.
- **Log every tool call.** Without a trace you cannot tell whether the model reasoned badly or the tool returned nonsense.

More agents is not better. Each additional autonomous hop multiplies the ways it can go wrong.

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