AI Agent

What is an AI Agent?

An AI agent combines a large language model with tools and a loop. You give it a goal, such as "find out why this customer's order is late and draft a reply." The model decides which step to take first, calls a tool to look up the order, reads the result, chooses the next step, and repeats until it has an answer or reaches a limit.

The tools are what make an agent useful. They can be web search, a database query, an API call into a CRM or helpdesk, a calculator, or a function that sends an email. Standards such as the Model Context Protocol make it easier to connect models to these tools in a consistent way.

In business, agents appear as research assistants that gather and summarize information, support agents that resolve routine tickets, operations agents that reconcile records across systems, and internal copilots that answer staff questions and take simple actions on their behalf.

How is an AI agent different from a chatbot?

A basic chatbot answers questions in a conversation. It may be very good with language, but it usually cannot act: it does not look anything up in your systems or change anything. An AI chatbot connected to your knowledge base can answer from your content, but it still responds rather than acts.

An agent takes steps. It decides which tools to use and in what order, and it can update a record, create a task or send a message. That makes it more capable and also riskier, because a wrong decision now has an effect outside the conversation.

In practice the line is blurry. Many customer-facing assistants are chatbots with a few agent abilities, such as checking an order status or booking a call, added for specific, low-risk actions.

What are AI agents good at today?

Agents work best on tasks that are multi-step but bounded, where the tools are well defined and mistakes can be caught. Good examples:

• researching a company or contact across several sources and writing a short brief;
• triaging an inbox: reading each message, classifying it, then filing it or drafting a reply;
• looking up data across systems to answer an internal question;
• reconciling records, such as matching payments to invoices and flagging exceptions;
• preparing a report by pulling figures from several tools.

They are weaker on long, open-ended tasks with many possible paths, where small errors compound and nobody checks the steps in between. Starting with bounded tasks also makes it easy to compare the agent's work with what a person would have done.

What are the risks of AI agents, and how do you control them?

Because agents act, their mistakes travel. An agent can misread a request, call the wrong tool, loop without finishing, or be steered by malicious instructions hidden in an email or web page it reads.

Controls come from design:

• give each agent the smallest set of tools and permissions it needs;
• require human-in-the-loop approval before irreversible actions such as payments, deletions or customer messages;
• set limits on steps, time and cost per task;
• log every tool call so you can see what happened and why.

Testing on realistic cases before launch, and reviewing logs after, is what turns an agent from a demo into a dependable part of operations. Approval steps can be relaxed later for actions the logs show the agent handles well.

Should you build an AI agent or a fixed workflow?

If you can draw the process as a flowchart that rarely changes, a fixed workflow with an AI step where judgment is needed is usually cheaper, faster and easier to test. Agents earn their place when the path genuinely varies from case to case: the right next step depends on what the previous lookup returned, and listing every branch in advance is impractical.

A sensible path is to start with the workflow, see which cases fall outside it, and hand only those cases to an agent with a narrow set of tools. As logs show the agent behaving well, its scope can widen. That keeps the predictable parts predictable and spends the flexibility, and the risk, only where it pays off.

How does Vizio AI build AI agents?

In our DX Studio we build agents as part of AI-powered internal workflows: research, classification, reporting, decision support and internal copilots that help teams act on information. We connect them to the CRM, finance, marketing, product and support tools a business already uses through API and systems integrations, keep their permissions narrow and log their actions. Where a plain workflow automation does the job, we recommend that instead. We support agents after launch and adjust them as tools and processes change.

We usually start by mapping the current workflow and choosing one bounded task where an agent can take steps safely, with a person reviewing the result.