What is AI Agent?
An AI agent is a program that lets a language model choose its own next action toward a goal, calling tools such as a browser, an HTTP client or a shell between reasoning steps and feeding each result back into its context. A fixed script sets the order in advance; an agent derives it from what the last step returned.
The runtime is a loop. The model receives the goal, the conversation so far and a set of tool definitions with typed inputs; it emits a tool call; the runtime executes that call and appends the result; the loop repeats until the model declares completion or hits a step, time or token budget. Everything hard about agents lives in that loop. Context grows with every result, so long runs get expensive and lose earlier detail. Agents also retry non-idempotent actions, and they pass arguments that satisfy a schema while making no sense.
Anything touching the open web hits infrastructure problems before it hits reasoning problems. Agents usually run on cloud VMs or serverless platforms whose address ranges are recognisable as hosting networks, and plenty of sites answer those with a challenge page or a stripped-down version of the page a person would see. An agent reporting that a page had no pricing on it is often reporting what a bot-management product chose to show it. Egress resembling an ordinary consumer connection removes that whole class of confusing failure.
Tooling has converged on a few pieces. The Model Context Protocol gives assistants a standard way to discover and call tools, and JSON Schema constrains what a model can pass into one. Browser automation libraries drive a real browser when a page needs JavaScript to render. Give each agent its own credentials rather than sharing a human's, and log every tool call with its arguments. Require approval before a write such as a payment, a deletion or an outbound message, because a mistaken read costs tokens while a mistaken write costs money.
Content an agent reads is untrusted input. A web page or a tool result can contain text written to redirect the agent, and the model has no built-in way to tell instruction from data. Practical defences are structural. Keep tool permissions narrow, separate the agent that reads untrusted content from the one holding credentials, cap steps and spend, and treat any instruction that arrived inside fetched content as something to report rather than obey.
Where you meet it
This stops being theoretical when your agent works perfectly on your laptop and then, deployed to a VM, starts insisting that pages are empty or products are out of stock. Fetch one of those pages yourself from the same host and you will usually find a challenge or a stripped-down variant waiting. How the agent reaches the internet is part of building it, not an afterthought you bolt on once the reasoning looks right.
Common questions
Why does my AI agent get blocked when browsing the web?
Most agents run in cloud environments, and those IP ranges are identifiable as hosting networks. Bot-management systems score them harshly before looking at behaviour, so the agent receives a challenge page or a reduced version of the site. The model then reasons over that page as if it were the real content, which produces confidently wrong answers.
What is the difference between an AI agent and a chatbot?
A chatbot produces text in reply to a message. An agent is given a goal and a set of tools, then decides which tool to call, reads the result and calls another, continuing until it finishes or hits a budget. The output is a completed task and a trail of actions rather than one response.
Do AI agents need proxies?
Only agents that read the public web from cloud infrastructure, or that need to see a site as a user in a specific country would. Agents working against your own APIs, internal databases or a local filesystem do not. Where a site varies by geography or sits behind bot management, egress location and network type change what the agent is shown.
Related terms
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