# The Spectrum of Autonomy — AI Agents and Tool Use

Source: https://www.geekswithgeeks.com/en/ai-agents-mcp/found-autonomy

> Place a system on the scale from plain assistant to autonomous agent and choose the right level.

## Not a yes/no label

"Agent" is a spectrum. At one end is a **plain assistant** that only answers. Next comes an assistant with **one or two tools**. Then a **workflow** where your code fixes the steps. At the far end is an **autonomous agent** that chooses its own steps and may run for many turns. More autonomy gives more power, and also more cost, risk and unpredictability.

## From answering to acting

An agent perceives a goal, reasons about what to do, acts through tools and learns from the result.

![Four stages: goal, reason, act, observe.](assets/figures/ai-agents-mcp/section-1-map.svg) — Figure 1.1 — Goal, reason, act and observe.

## A quick decision guide

Use the lowest level that solves the problem. Move right only when a simpler design measurably falls short.

```text
Answer from knowledge only        -> plain assistant
Need 1-2 lookups or actions        -> assistant + tools
Steps are known in advance         -> workflow (code controls flow)
Steps depend on what is found      -> agent loop
Long-running, many tools, risky    -> agent + approvals + budgets
```

## Cab, bus and self-drive

A bus follows a fixed route (workflow). A cab driver picks the route given a destination (agent). Hiring a cab for a bus route costs more, and trusting a driver you cannot watch needs seat-belts and rules.

**Quiz:** When should you prefer a fixed workflow over a full agent?

- [ ] Never
- [ ] When you want more risk
- [x] When the steps are known in advance
- [ ] When tools do not exist

*Answer:* When the steps are known in advance. Known steps do not need the model to decide them, so a workflow is cheaper and more predictable.