The loop is the important part
A normal chat response is usually one request followed by one answer. An agent can break a goal into steps, select a tool, inspect the result, change its plan, and continue until it is done or needs a person.
A simple loop is: plan, act, observe, adjust, repeat. The tools may include search, files, code execution, APIs, or a computer interface.
An agent is a system, not only a model
The model helps choose the next step. The surrounding system controls what information it receives, which tools are available, what permissions those tools have, how long the task can run, and what gets recorded.
That surrounding design determines whether an agent is a useful assistant or an unpredictable risk. A strong model inside a careless system is still a careless system.
More autonomy requires tighter boundaries
Agents can misunderstand intent, follow malicious instructions hidden in outside content, use the wrong account, or take an action that is difficult to reverse. Good designs limit access and separate research from consequence.
- Give each tool only the permission required for its job.
- Require approval before sending, publishing, paying, deleting, or changing an important record.
- Show the person exactly what will happen before approval.
- Keep a record of the request, sources, decision, and result.
- Provide a simple way to stop or disable the workflow.
Where MCP fits
Model Context Protocol, or MCP, is one way for an AI host to connect to focused servers, tools, and resources. It does not make a model all-powerful. The host still coordinates permission and each server should receive only the context needed for its function.
When someone demonstrates an agent, ask to see its tool list, permissions, approval points, failure behavior, and activity record. Those details matter more than the perfect demo run.
Sources and further reading
Product capabilities and policies change. These are the primary sources used for this guide.